Heterogeneous dual-stream contrastive adversarial network for cross-domain mechanical fault diagnosis

Abstract

In the cross-machine fault diagnosis task of bearings for rotating machinery, inherent physical differences among different mechanical devices cause severe domain distribution shift of fault features. Consequently, the diagnostic model trained on the source machine suffers a dramatic decline in generalization performance when applied to diagnosis tasks of target machines, making it difficult to meet the requirements of practical industrial diagnosis. To address this issue, this paper proposes a heterogeneous dual-stream contrastive adversarial network (HDCAN). Firstly, one-dimensional (1D) vibration signals are converted into two-dimensional images via the symmetric dot pattern, constructing a signal-image multimodal input system. Secondly, a parallel dual-stream network architecture is designed, and a cross-modal contrastive learning strategy is introduced simultaneously to effectively eliminate the negative transfer effect between heterogeneous modalities and realize the pre-alignment of multimodal features. On this basis, the domain adversarial learning mechanism is integrated to further extract generalized features with both fault discriminability and domain invariance, so as to improve the cross-machine adaptation capability of the model. To verify the effectiveness of the proposed method, experiments are conducted on six cross-machine diagnosis tasks from three public bearing fault datasets. The experimental results show that the average diagnostic accuracy of the HDCAN model reaches 92.10%, which significantly outperforms the current mainstream domain adaptive fault diagnosis methods. The experimental results fully demonstrate that the proposed HDCAN can effectively alleviate the distribution shift problem of cross-machine fault data and possesses great application potential in industrial practical scenarios.

Keywords

Cross-machine contrastive learning domain adaptation fault diagnosis symmetrized dot pattern

Introduction

As core components of modern industrial systems, rotating machinery plays a critical role in determining the machining quality and stability of computer numerical control machine tools.^1,2 Rolling element bearings represent one of the most vulnerable critical parts within such equipment. Therefore, conducting regular condition monitoring and fault diagnosis on bearings facilitates the timely detection of incipient fault signatures, enables the implementation of preventive maintenance measures, and ensures the reliable operation of rotating machinery.³

In recent years, to address the complex and variable working conditions in real-world industrial scenarios, researchers have increasingly focused on the deep mining and joint analysis of time-domain and frequency-domain information. For instance, Ma et al.⁴ proposed the TFD-Former framework based on a convolutional neural network (CNN)-Transformer architecture, which achieves precise alignment and efficient fusion of time-frequency domain features through customized contrastive learning loss and cross-attention mechanisms. Aiming at the domain shift problem caused by variable rotational speeds, Pang et al.⁵ developed a time-frequency supervised contrastive learning framework to extract speed-immune, class-invariant features by learning the physical consistency of the time-frequency information in bearing signals. Furthermore, to overcome the challenge of scarce labeled data, He et al.⁶ introduced a self-supervised learning model based on time-frequency dual-domain contrast and fusion, utilizing a dual-encoder and a time-frequency fusion encoder to effectively capture the global and local nonlinear dynamic features of the signals. Although these methods have demonstrated significant efficacy in specific scenarios, their practical applications are still bottlenecked by the reliance of traditional time-frequency analysis on the stationarity assumption, presenting the following shortcomings: (1) In the time-frequency representations generated by short-time Fourier transform or continuous wavelet transform, the amplitude and frequency distributions fluctuate violently with changes in equipment speed or load, leading to severe feature drift; (2) Traditional time-frequency analysis heavily emphasizes amplitude distribution, making weak early fault features susceptible to being masked by strong background noise; (3) Such preprocessing methods are highly dependent on the intervention of expert experience and prior knowledge.

Compared with traditional time-frequency analysis methods, the symmetric dot pattern (SDP),^7,8 serving as an intuitive signal representation strategy, can directly map 1D time-series signals into mirror-symmetric, snowflake-like two-dimensional (2D) images. This method is not only easy to implement with low computational overhead but also theoretically circumvents the heavy reliance of traditional time-frequency analysis on the stationarity assumption of signals. To date, several studies have validated the effectiveness of SDP under more stringent working conditions. For instance, Wang et al.⁹ combined SDP with CNNs, demonstrating that the method yields high diagnostic accuracy and fault localization capabilities. Tang et al.¹⁰ fused multi-sensor information using a color-SDP approach and designed a coarse-to-fine diagnostic framework, successfully achieving motor fault diagnosis in highly noisy environments. Hao et al.¹¹ utilized the SDP method to convert time-series signals into 2D images to enhance feature representation, realizing accurate diagnosis under transient speed conditions. The aforementioned research has preliminarily verified the substantial potential of SDP in complex environments, such as those with strong noise and transient conditions. However, in actual industrial production, mechanical equipment frequently operates under variable working conditions, and the training and testing data often originate from different physical machines. In the presence of significant distribution shifts between the source and target domains, the generalization performance of existing models often experiences a sharp decline, making it difficult to meet the stringent demands for high reliability in industrial fields. Therefore, how to overcome spatial distribution discrepancies to achieve robust cross-condition and cross-machine fault diagnosis remains an open issue worthy of further in-depth exploration.

With the successful application of transfer learning (TL) in fields such as computer vision and natural language processing,^12–14 TL-based intelligent fault diagnosis has demonstrated the capability to achieve effective end-to-end fault detection and has gradually emerged as a research hotspot. For instance, Li et al.¹⁵ proposed a deep continual TL framework named deep continual transfer learning network with dynamic weight aggregation (DCTLN-DWA), capable of effectively handling industrial streaming data under varying working conditions. Li et al.¹⁶ introduced a novel unsupervised TL method combining joint distribution and adversarial networks for the fault diagnosis of bearings and gears in rotating machinery. Similarly, Li et al.¹⁷ proposed a TL approach utilizing a generative adversarial network, which demonstrated satisfactory fault diagnosis performance under imbalanced data conditions.

As a feasible solution, domain adaptation (DA) techniques have extended TL methods from academic research to industrial applications. The key to DA-based methods lies in transferring diagnostic knowledge learned from labeled training data (source domain) to unlabeled testing data (target domain), thereby mitigating the domain shift problem. Existing mainstream DA methods can be broadly categorized into two types: distance metric-based methods and adversarial learning-based methods. First, metric-based methods employ statistical distances to measure the similarity between the source and target domains, explicitly assessing and minimizing distributional discrepancies. Second, adversarial-based methods design adversarial training between a feature extractor and a domain discriminator to implicitly induce domain-invariant features. Regarding metric-based approaches, Qin et al.¹⁸ proposed a statistical metric method combining multi-kernel maximum mean discrepancy (MMD) and correlation alignment (CORAL). By simultaneously evaluating the mean and covariance between source and target domains, this method alleviated domain distribution drift under different operating conditions. Lin et al.¹⁹ introduced an information fusion-enhanced DA attention network, incorporating a weight assignment module and a joint Maximum Mean Discrepancy metric strategy to mitigate the impact of negative transfer. Wang et al.²⁰ proposed a multi-target domain adaptive network based on transfer graph feature alignment, which was validated under varying rotational speeds and load conditions. Additionally, Wang et al.²¹ presented a domain reinforcement feature adaptation method based on CORAL, addressing the cross-domain learning problem in compound fault diagnosis of rolling bearings. Yang et al.²² proposed a Point-to-Set Metric-Gated Mixture of Experts model, combining multi-layer MMD measurements for domain alignment to ensure feature alignment across different domains.

From the above work, it is evident that existing intelligent fault diagnosis methods based on TL primarily focus on transfer between different operating conditions (load, speed). These studies demonstrate that TL enables intelligent fault diagnosis methods to be applied to datasets obtained from machines operating under diverse conditions. In industrial settings, annotated data for precision-critical and confidential machinery are scarce or even absent due to scheduled maintenance, limiting the application of these DA methods. Consequently, cross-machine transfer fault diagnosis becomes indispensable. Only when cross-machine transfer fault diagnosis functions effectively can intelligent methods trained on labeled data from other machines accurately assess the health status of machines with unlabeled data. Recently, researchers^23–25 have begun to explore cross-machine fault diagnosis to facilitate practical industrial applications. This approach extracts diagnostic knowledge from machines with available fault data and transfers it to new machines. However, the diagnosis requires mitigating data shifts caused by variations in machine structures, bearing types, and working conditions, rendering it both a challenging and practically significant endeavor. For instance, Shao et al.²⁶ proposed a joint distribution alignment model based on information theory, which enhances the robustness of prediction results by introducing virtual adversarial training, thereby effectively improving the transferability of cross-device fault diagnosis. Liu et al.²⁷ proposed an adaptive source-free DA method named Adaptive Intra/Inter-domain Information Maximization (AI3M) for mechanical fault detection, which improves cross-machine performance by reducing both intra-domain and inter-domain distribution shifts. Yan et al.²⁸ introduced an enhanced cross-machine transfer strategy with multi-source domain knowledge (CMMK), boosting performance in cross-device diagnosis through multi-source collaborative training, adversarial training, and a custom threshold module. Kang et al.²⁹ proposed a mechanism-constrained cross-domain fault diagnosis framework, which achieves superior performance in cross-machine fault diagnosis tasks. Gu et al.³⁰ put forward a dynamic adversarial joint DA framework to improve the diagnostic adaptability of heterogeneous mechanical equipment. Miao et al.³¹ designed a dual feature constraint-based three-component decoupled DA network. This method is capable of effectively extracting domain-invariant features.

Despite these advancements, several issues remain unresolved regarding reliable DA across highly discrepant domains in practical applications. On one hand, most existing studies rely on convolutional layers with limited receptive fields to extract local fault representations. They fail to capture useful global diagnostic knowledge, resulting in extracted features with insufficient generalization capability. Consequently, such methods often exhibit suboptimal generalization performance when signal distribution shifts become severe. On the other hand, the key to mitigating cross-machine signal shifts lies in addressing distribution alignment and knowledge transfer between the target and source domains. However, the majority of research remains confined to single-modal signal inputs, neglecting the complementary information available from multiple views. This oversight limits the model’s discriminative ability regarding complex fault patterns, thereby adversely affecting the accuracy of cross-machine diagnosis.

To address the aforementioned limitations, this paper proposes a novel heterogeneous dual-stream contrastive adversarial network (HDCAN). Firstly, to enrich the representational dimensions of fault features, the SDP transformation technique is introduced. This technique maps 1D vibration signals into 2D mirror-symmetric images within a polar coordinate system, thereby explicitly enhancing the geometric structural features of the signals. Building upon this, HDCAN constructs a parallel heterogeneous dual-stream feature extraction architecture, comprising a geometric-aware image branch and a multi-scale convolution-based time-domain signal branch. Secondly, to bridge the discrepancy between heterogeneous modal representations, a cross-modal contrastive learning strategy is designed. This strategy enforces high-level representations of the same-source samples in both the geometric and time domains to be pulled closer, achieving deep alignment of heterogeneous features. Finally, by integrating a domain adversarial learning module, the model extracts generalized features possessing both fault discriminability and domain invariance through the interplay between the feature extractor and the domain discriminator. The main contributions of this work are summarized as follows:

A specific HDCAN framework tailored for cross-machine fault diagnosis is proposed. We provide an in-depth analysis of the performance of SDP transformation under different mechanical equipment conditions. It is demonstrated that when rotational speeds or loads undergo drastic variations, SDP images provide crucial complementary geometric features, effectively resolving the issue of inadequate representation inherent in single-modal information.

A heterogeneous dual-stream feature extractor integrating a geometric-aware image branch and a time-domain signal branch is designed. Furthermore, a cross-modal contrastive learning mechanism is innovatively incorporated to realize the adaptive fusion of multi-view features.

End-to-end feature adaptation is achieved via domain adversarial learning. In six cross-machine diagnosis tasks constructed from three distinct mechanical equipment, HDCAN consistently achieves diagnostic performance superior to mainstream DA methods, verifying its generalization potential and practical application value in real-world industrial cross-device scenarios.

Theoretical background

Mathematical framework of SDP analysis

SDP is a method for converting time signals into images, and the differences among different signals can be reflected through the variations in the shapes of snowflake petals.³² This structure is more sensitive to periodic shocks and can enhance the visualization expression ability of fault frequency components.³³ For the time-domain signal $X = {x_{1}, x_{2}, \dots, x_{n - 1}, x_{n}}$ , it is first normalized, and then the time-domain signal is transformed into the polar coordinate system through the method of generating symmetrical points, resulting in the signal $S (r_{i}, θ_{i}, ϕ_{i})$ . Figure 1 shows how to draw an SDP image from a vibration signal,^34,35 and the relevant mapping function is as follows:

r (i) = \frac{x_{i} - x_{\min}}{x_{\max} - x_{\min}}

(1)

θ (i) = θ + \frac{x_{i + l} - x_{\min}}{x_{\max} - x_{\min}} ξ

(2)

ϕ (i) = θ - \frac{x_{i + l} - x_{\min}}{x_{\max} - x_{\min}} ξ

(3)

where $x_{\max}$ represents the maximum amplitude of the signal, $x_{\min}$ represents the minimum amplitude of the signal, and $x_{i}$ represents the $i$ th sample point of the signal. $l$ represents the time interval parameter, $θ$ represents the rotation angle of the mirror symmetry plane, and $ξ$ represents the angle amplification factor.

Figure 1.

The principle of SDP method: (a) time series, (b) the principle of signal into image, and (c) SDP image. SDP: symmetric dot pattern.

SDP images with sine signals at frequencies of 100, 200, and 400 Hz, as well as Gaussian white noise at 10 dB $(θ = 60^{°}, ξ = 40^{°}, l = 5)$ are shown in Figure 2. From the mathematical expression of the SDP transform, the focus of SDP analysis is on locating points $(r_{i}, θ_{i}, ϕ_{i})$ on the polar coordinate plane S. Differences in signal frequency or amplitude primarily manifest as variations in the position and curvature of points on the polar plane. Essentially, the SDP image serves as a visual fingerprint of a signal’s temporal and phase characteristics. Orderly, symmetrical patterns indicate well-functioning signals with periodicity or time-reversibility, potentially corresponding to healthy bearing conditions. Conversely, chaotic or scattered patterns typically point to anomalies, noise, or faults that disrupt the inherent predictability of the signal. The observed distinction between the sine wave and Gaussian noise in Figure 2 highlights the SDP image’s capability to differentiate structured signal behavior from random signal behavior. Consequently, SDP analysis utilizing polar space SDP images provides an intuitive representation of frequency and amplitude variations within time series, further reflecting fault states in diverse mechanical equipment. In this study, SDP analysis will also be employed to visualize bearing vibration signals.

Figure 2.

The SDP images of (a) 100, (b) 300, and (c) 500 Hz sine signals and (d) 10 dB Gaussian white noise signals. Green and blue, respectively, represent the phase trajectories of the signal during its forward and backward temporal evolution. Their symmetrical relationship reflects the inherent time reversibility and periodicity characteristics of the signal. SDP: symmetric dot pattern.

Contrastive learning

Contrastive learning is a form of self-supervised learning, which aims to learn the representation of samples through the similarity contrast between samples.³⁶ Distinct from traditional generative self-supervised methods, contrastive learning aims to pull positive sample pairs closer together in the embedding space while pushing negative pairs apart by measuring the similarity between samples. In fault diagnosis tasks, to capture discriminative fault features, an encoder network is typically trained to map input samples into low-dimensional feature vectors.

For a given query sample $x_{q}$ and its corresponding feature vector $q = f (x_{q})$ , the objective of contrastive learning is to identify its corresponding positive key $k_{+}$ . This positive key usually represents a different augmented view or a different modal representation of the same fault sample. Simultaneously, the model must distinguish $q$ from a set of negative keys ${k_{-}}$ , which are samples originating from other fault categories. A specific InfoNCE^37,38 loss function is employed to evaluate the contrastive loss. For a training batch containing $N$ negative samples, its general mathematical formulation is expressed as:

L_{NCE} = - \log \frac{\exp (sim (q, k_{+}) / τ)}{\exp (sim (q, k_{+}) / τ) + \sum_{i = 1}^{N} \exp (sim (q, k_{i}^{-}) / τ)}

(4)

where $sim (u, v) = \frac{u^{⊤} v}{∥ u ∥ ∥ v ∥}$ denotes the cosine similarity between two feature vectors, measuring their directional consistency; $k_{+}$ represents the matching positive sample feature for $q$ ; $k_{i}^{-}$ denotes the $i$ th unmatched negative sample feature; and $τ$ is the temperature parameter. This parameter is used to regulate the model’s focus on hard negative samples. By minimizing the aforementioned loss function, the encoder is optimized to learn a feature representation capable of maximizing the mutual information between samples.

Framework of the proposed fault diagnosis method

Problem setup

In practical industrial settings, different mechanical equipment exhibits significant spatial variations in collected vibration signals due to differences in structural parameters and operating conditions, even when experiencing the same fault type. Treating different mechanical equipment as distinct domains, cross-machine fault diagnosis fundamentally constitutes a DA problem involving spatial distribution shifts. To address this practical challenge, the research object of this paper is focused on rolling bearings, the core component of rotating machinery. The application scenario is targeted at unsupervised cross-domain fault diagnosis across different mechanical equipment in industrial sites, where no labeled vibration data are available in the target domain. This distribution discrepancy prevents feature extractors and classifiers trained on source domain data from directly generalizing to target domain data. Therefore, the core challenge in cross-machine fault diagnosis lies in how to fully leverage labeled data on the source machine while effectively transferring the diagnostic model to the target machine.

To clarify the definition of this task, this paper adopts the diagnostic paradigm of end-to-end unsupervised fault classification. As illustrated in Figure 3, to define the target task, we have $N_{s}$ labeled source domain samples $D_{s} = {(x_{s}^{i}, y_{s}^{i})}_{i = 1}^{N_{s}}$ , where $x_{s}^{i}$ represents the raw vibration signal and $y_{s}^{i}$ denotes its corresponding fault category label which covers typical health conditions including normal state (N), ball fault (BF), inner race fault (IF), and outer race fault (OF). Additionally, we have $N_{t}$ unlabeled target domain samples $D_{t} = {x_{t}^{j}}_{j = 1}^{N_{t}}$ . The source domain and target domain share the same fault category space, but their marginal data distributions differ, that is, $P_{s} (x_{s}) \neq Q_{t} (x_{t})$ . The objective of cross-machine diagnosis is to learn a feature mapping function $ϕ$ and a classifier $f$ using labeled samples from the source domain $D_{s}$ , such that the fault classification error is minimized on the target domain $D_{t}$ . This objective can be described as:

\min_{ϕ, f} L_{cls} (D_{s}) + λ \cdot d (P_{s} (ϕ (X)), Q_{t} (ϕ (X)))

(5)

where $L_{cls}$ denotes the source domain classification loss, $d$ represents a distance metric measuring the difference in feature distributions between the source and target domains, and $λ$ is the balancing coefficient.

Figure 3.

Schematic illustration of the cross-machine fault diagnosis problem setup.

Overall framework

As shown in Figure 4, the proposed HDCAN is designed to address the spatial distribution shift problem in unsupervised bearing fault diagnosis across machines. Its overall architecture consists of four core modules: A. SDP transformation, B. Heterogeneous dual-stream feature extraction, C. Cross-modal contrastive learning, and D. Domain adversarial learning strategy.

Figure 4.

Framework of HDCAN. HDCAN: heterogeneous dual-stream contrastive adversarial network.

First, the original 1D vibration signal $(X_{seq})$ is divided into two parallel processing streams. One stream is directly fed into the time-domain signal branch, while the other is mapped into a 2D SDP image $(X_{img})$ via SDP transformation. Thereby, a signal-image heterogeneous input is constructed to provide complementary information.

Subsequently, in the heterogeneous dual-stream feature extraction stage, the time-domain signal branch adopts multi-scale convolution and physical statistical feature calculation to capture local and global dynamic features of temporal data, generating the signal feature vector $(V_{signal})$ . Meanwhile, the geometry-aware image branch extracts the basic feature map $(F_{base})$ of the image through a pre-trained backbone network. It further focuses on key feature regions via the geometric feature enhancement module and spatial attention mechanism in sequence, and outputs the image feature vector $(V_{img})$ .

To eliminate the distribution discrepancy between the two heterogeneous modalities, the cross-modal contrastive learning module projects $V_{img}$ and $V_{signal}$ into a unified latent representation space through a nonlinear mapping network (mapping-layer normalization (LN)-gaussian error linear unit (GELU)), yielding $Z_{img}$ and $Z_{signal}$ . The cross-modal contrastive learning (CL) loss $(L_{CL})$ based on InfoNCE is employed to maximize the similarity of cross-modal representations of homologous samples, so as to achieve efficient pre-alignment of features and generate the global feature $(F_{final})$ fused with dual-stream information.

Finally, in the domain adversarial learning strategy module, the fused feature $F_{final}$ flows into two jointly optimized branches. On the one hand, it is fed into the fault classifier to calculate the classification loss $(L_{cls})$ , ensuring the discriminative capability of features for different fault types. On the other hand, it passes through a gradient reversal layer (GRL) and is input into the domain discriminator to compute the domain adversarial loss $(L_{domain})$ , so as to learn device-invariant domain representations. Through joint optimization, the entire network end-to-end learns general features with both cross-machine domain invariance and strong fault discriminability, enabling high-precision unsupervised fault diagnosis in industrial scenarios.

SDP representation of bearing vibration signals for different machinery equipment

To evaluate the visualization effectiveness of the SDP method on bearing vibration signals from different mechanical equipment, this study selected bearing data from three distinct yet related mechanical systems: the Case Western Reserve University (CWRU) Data Center,³⁹ Jiangnan University (JNU),⁴⁰ and Xi’an Jiaotong University (XJTU).⁴¹ The bearing vibration signals primarily encompass four bearing conditions: Normal (N), Ball Fault (BF), Inner Ring Fault (IF), and Outer Ring Fault (OF). To better distinguish bearing states, the parameters $θ$ , $ξ$ , and $l$ in SDP analysis are adjusted. With $θ$ typically set to 60° (n = 6), the mirror symmetry planes of the SDP image are 0, 60, 120, 180, 240, and 300°.^9,42 The resulting image exhibits six snowflake-like petals. Subtle signal distinctions primarily depend on parameters $ξ$ and $l$ . Appropriate $ξ$ and $l$ values effectively enhance image resolution and amplify signal differences. Therefore, this paper adopts the principle of maximizing image dissimilarity to calculate image similarity,⁴³ as shown in Figure 5, with parameters set to $ξ$ = 40° and $l$ = 5.

Figure 5.

Relationship between $ξ, l$ and image similarities.

Raw time-series data were acquired from various mechanical equipment, encompassing vibration signals under different rotational speeds and load conditions. Using Equations (1)–(3), the original bearing vibration signals were transformed into SDP images corresponding to parameters $ξ$ = 40° and $l$ = 5, as shown in Figure 6. It can be observed that SDP transforms amplitude fluctuations and frequency components in time-series signals into highly recognizable geometric structures. For instance, in the JNU dataset shown in Figure 6(b), the IF signal exhibits intense amplitude fluctuations due to strong frequency modulation, resulting in an SDP image characterized by a significantly thickened, dense-textured, and full-bodied morphology. In contrast, the OF, characterized by relatively stable impacts, yields an SDP image with a sparse point cloud distribution and a skeletal geometric structure. This pronounced inter-class geometric distinction demonstrates that SDP distinguishes fault modes by capturing image texture features rather than relying solely on temporal domain metrics. Further comparison of Figure 6(a) to (c) reveals that despite operational variations across domains, N consistently maintains optimal convergence and clarity, while IF exhibits the highest amplitude dispersion. This relative stability in cross-domain feature performance indicates that SDP mapping effectively mitigates interference from operational parameter variations during feature extraction, translating fundamental fault differences into robust visual patterns. This further facilitates cross-domain feature alignment for the HDCAN model, aiding subsequent bearing condition recognition.

Figure 6.

Vibration signals and SDP images of three mechanical devices under different rotational speeds and load conditions: (a) vibration signals and SDP images of BF, IF, OF, and N under CWRU datasets; (b) vibration signals and SDP images of BF, IF, OF, and N under JNU datasets; (c) vibration signals and SDP images of BF, IF, OF, and N under XJTU datasets. SDP: symmetric dot pattern; N: Normal; BF: Ball Fault; IF: Inner Ring Fault; OF: Outer Ring Fault; CWRU: Case Western Reserve University; JNU: Jiangnan University; XJTU: Xi’an Jiaotong University.

Heterogeneous dual-stream feature extraction

To capture the characteristics of mechanical equipment faults across different domains, this paper proposes a heterogeneous dual-stream feature extraction architecture. As shown in Figure 7, this architecture comprises two parallel branches: the geometric-aware image Branch and the time-domain signal branch. The former is designed to extract spatial geometric features from transformed SDP images, whereas the latter focuses on mining multi-scale temporal features from raw vibration signals.

Figure 7.

Structure of heterogeneous dual-stream feature extraction.

Geometric-aware image branch

SDP images visually represent the structural characteristics of vibration signals within the polar coordinate system. To effectively extract visual patterns from these images, we design a geometric-aware feature extractor based on EfficientNetB0. Specifically, an EfficientNetB0 model pre-trained on ImageNet is employed as the backbone network to extract shallow generic texture features. Let the input SDP image be denoted as $X_{img}$ , and the output feature map of the backbone network be represented as $F_{base}$ .

To further reinforce the geometric structural representation of fault patterns, a Geometric Feature Enhancement Module is introduced following the backbone network. This module comprises two cascaded convolutional layers, employing the GELU activation function and L2 regularization to prevent overfitting. Subsequently, a Spatial Attention Mechanism is incorporated to adaptively weight salient feature regions. The attention weight map $A_{att}$ is computed as follows:

A_{att} = σ (W_{att} * F_{geo} + b_{att})

(6)

F_{enhanced} = F_{geo} ⊙ A_{att}

(7)

where, $F_{geo}$ denotes the output of the geometric enhancement convolutional layers, * represents the convolution operation, and $σ$ refers to the Sigmoid activation function. Furthermore, $W_{att}$ and $b_{att}$ denote the weight and bias of the attention convolution kernel, respectively, while ⊙ indicates element wise multiplication. Consequently, $F_{enhanced}$ is obtained as the enhanced feature after attention weighting.

To obtain a global feature representation, both global average pooling (GAP) and global max pooling (GMP) are simultaneously employed to reduce the dimensionality of the feature maps. Subsequently, the final image feature vector $V_{img}$ is generated through a feature fusion layer, formulated as:

V_{img} = LN (ϕ (W_{f} [GAP (F_{enhanced}) \oplus GMP (F_{enhanced})] + b_{f}))

(8)

where ⊕ denotes the feature concatenation operation. $W_{f}$ and $b_{f}$ represent the parameters of the fully connected layer, while $ϕ$ and $LN$ refer to the GELU activation function and Layer Normalization, respectively.

Time-domain signal branch

Considering that mechanical fault signals typically exhibit complex non-stationary impulsive components and periodic patterns, single-scale convolutional networks struggle to simultaneously capture local transient features and global dependencies. Therefore, a time-domain feature extraction branch integrating multi-scale convolutions and statistical features is designed.

The multi-scale convolutional feature extraction module comprises convolutions of three distinct scales: large, medium, and small ( $k \in {L, M, S}$ ), which are designed to capture temporal features across different receptive fields. Let the input 1D vibration signal be denoted as $X_{seq}$ . The feature extraction process is formulated as:

F_{scale}^{k} = P_{global} (B (C_{2}^{k} (P_{local} (B (C_{1}^{k} (X_{seq}))))))

(9)

where $C_{1}^{k}$ and $C_{2}^{k}$ represent the convolution operations, and $B$ denotes batch normalization (BN). Additionally, $P_{local}$ refers to local max pooling, while $P_{global}$ stands for GAP.

In cross-domain scenarios, high-order statistical features of signals exhibit high sensitivity to weak transient impulses induced by incipient faults. To compensate for the limited physical interpretability of neural networks, physical statistical features $V_{stat}$ are further calculated. Taking Kurtosis ( $K$ ) and Impulse Factor ( $IF$ ) as examples, their definitions are given as follows:

K = \frac{1}{N} \sum_{i = 1}^{N} {(\frac{x_{i} - μ}{σ})}^{4}

(10)

IF = \frac{\max | x_{i} |}{\frac{1}{N} \sum_{i = 1}^{N} | x_{i} |}

(11)

where $x_{i}$ represents the amplitude of the $i$ th sampling point, $μ$ and $σ$ denote the mean and standard deviation, respectively, and $N$ is the signal length. Subsequently, the statistical feature vector $V_{stat}$ , after being mapped through a fully connected layer, is fused with the convolutional features from the aforementioned three scales:

V_{signal} = F_{fusion} ([F_{scale}^{L}, F_{scale}^{M}, F_{scale}^{S}, φ (V_{stat})])

(12)

where $F_{fusion}$ denotes an adaptive fusion network incorporating Dropout and Layer Normalization, and $φ$ represents a nonlinear mapping transformation. The resulting $V_{signal}$ serves as the final signal feature vector that integrates multi-scale temporal dynamics with physical prior knowledge.

Cross-modal contrastive learning and feature fusion

As shown in Figure 8, to prevent the heterogeneous modalities 1D vibration signals and SDP images from triggering negative transfer during the subsequent domain adversarial learning process, this paper develops a heterogeneous modality negative transfer suppression mechanism based on cross-modal contrastive learning prior to the feature fusion stage. Specifically, this mechanism utilizes cross-modal contrastive learning to project the feature representations of the 1D signals and SDP images into a unified metric embedding space. By minimizing the cross-modal feature distance of the same sample, it pre-eliminates the distribution discrepancy introduced by modality heterogeneity before fusion. Subsequently, multi-modal feature fusion is executed to ensure that the fused representations exclusively encapsulate the cross-machine domain shift caused by physical equipment variances. This strategy empowers the subsequent domain adversarial module to focus solely on cross-machine distribution alignment, isolating the interference stemming from heterogeneous modal differences and thereby fundamentally averting the occurrence of negative transfer.

Figure 8.

Structure of cross-modal contrastive learning.

First, to map image and signal features of distinct dimensions into a common metric subspace of uniform dimensionality, independent non-linear projection heads are designed for each branch. The projection process is defined as follows:

Z_{img} = P_{v} (V_{img}) = W_{v 2} (δ (LN (W_{v 1} V_{img})))

(13)

Z_{signal} = P_{s} (V_{signal}) = W_{s 2} (δ (LN (W_{s 1} V_{signal})))

(14)

where $P_{v}$ and $P_{s}$ denote the projection mapping functions for the image and signal branches, respectively. $W_{v 1}, W_{v 2}$ and $W_{s 1}, W_{s 2}$ represent the learnable weight matrices. In terms of architecture, the projection head comprises fully connected layers, the GELU activation function $δ$ , Layer Normalization $LN$ , and Dropout operations. The resulting $Z_{img}, Z_{signal}$ serve as the embedding vectors within the common subspace.

Furthermore, to ensure that image and signal embeddings from the same fault sample are pulled closer together, while those from distinct samples are pushed apart, a cross-modal contrastive loss function is designed. Specifically, $L_{2}$ normalization is applied to the embedding vectors to preserve directional information:

{\begin{matrix} {\hat{z}}_{img} = \frac{Z_{img}}{∥ Z_{img} ∥_{2}} \\ {\hat{z}}_{signal} = \frac{Z_{signal}}{∥ Z_{signal} ∥_{2}} \end{matrix}

(15)

For a training batch containing $N$ samples, the cosine similarity between the image feature ${\hat{z}}_{img}^{(i)}$ of the $i$ th sample and the signal feature ${\hat{z}}_{signal}^{(j)}$ of the $j$ th sample is defined as:

sim (i, j) = \frac{{({\hat{z}}_{img}^{(i)})}^{⊤} \cdot {\hat{z}}_{signal}^{(j)}}{τ}

(16)

where $τ$ denotes the temperature parameter. It regulates the model’s focus on hard negative samples and is set to 0.1 in this study.

To maximize the mutual information of the positive pairs $(i, i)$ , a variant of the InfoNCE loss function is adopted as the cross-modal contrastive loss $L_{CL}$ . For the $i$ th sample within a training batch, the loss is calculated as follows:

ℓ_{i} = - \log \frac{\exp (sim (i, i))}{\sum_{k = 1}^{N} \exp (sim (i, k))}

(17)

The total contrastive loss is defined as the average over the entire batch:

L_{CL} = \frac{1}{N} \sum_{i = 1}^{N} ℓ_{i}

(18)

By minimizing the feature distance between the image and signal domains for the same equipment state, this loss function compels the model to learn modality invariant feature representations, thereby enhancing feature robustness against cross-domain environmental variations.

By minimizing the feature distance between the image and signal domains for the same equipment state, this loss function compels the model to learn modality-invariant feature representations, thereby enhancing feature robustness against cross-domain environmental variations. To preserve the richest original information, the fusion stage utilizes the pre-projection features, $V_{img}$ and $V_{signal}$ . Features from these two branches are concatenated along the channel dimension and integrated through a deep fusion network:

V_{joint} = [V_{img} \oplus V_{signal}]

(19)

F_{final} = ϕ_{fuse} (D (LN (W_{fuse} V_{joint} + b_{fuse})))

(20)

where ⊕ denotes the concatenation operation, while $W_{fuse}$ and $b_{fuse}$ represent the parameters of the fusion layer. $LN$ refers to layer normalization, $D$ indicates the Dropout layer, and $ϕ_{fuse}$ denotes the GELU activation function.

The ultimately obtained $F_{final}$ not only integrates the geometric structural information from SDP images with the physical statistical information from time-domain signals but also enhances feature consistency through the constraints of contrastive learning. This fused representation is subsequently fed into the classifier and the domain adversarial discriminator to achieve high-precision cross-domain fault diagnosis.

Domain adversarial learning strategy and total optimization objective

Due to significant variations in operating conditions across different mechanical equipment, the fused features $F_{final}$ may still harbor domain-specific distribution biases. To eliminate these distributional discrepancies and enable the model to learn domain-invariant generalized fault features, this paper incorporates a domain adversarial learning strategy based on a GRL. Specifically, a domain discriminator $G_{d}$ is appended subsequent to the fused features $F_{final}$ . This discriminator is constructed from a multi-layer fully connected network, with Dropout layers introduced to enhance robustness. To facilitate adversarial training during backpropagation, a GRL is embedded between the feature extraction layer and the discriminator. Letting the input feature be denoted as $f = F_{final}$ , the mathematical operations of the GRL are described as follows:

R (f) = f

(21)

\frac{\partial R}{\partial f} = - λ_{adv} I

(22)

where $I$ represents the identity matrix, and $λ_{adv}$ serves as the DA coefficient, employed to regulate the intensity of the back-propagated adversarial gradients.

The domain discriminator $G_{d}$ accepts the features processed via the GRL and outputs the domain prediction probabilities. Let the domain label be denoted as $d_{i} \in {0, 1}$ (where 0 represents the source domain and 1 denotes the target domain). The discriminator loss function $L_{domain}$ is defined as the standard binary cross-entropy loss:

L_{domain} = - \frac{1}{N} \sum_{i = 1}^{N} [d_{i} \log G_{d} (f_{i}) + (1 - d_{i}) \log (1 - G_{d} (f_{i}))]

(23)

where $N$ is the batch size, and $f_{i}$ represents the fused feature of the $i$ th sample. Subsequently, the fused features $F_{final}$ are fed into the fault classifier $G_{y}$ . Composed of fully connected layers, Layer Normalization, and a Softmax output layer, this classifier aims to minimize the fault identification error on source domain samples. Let $y_{i}$ denote the ground truth fault label of the source domain sample, and $C$ represent the number of fault classes. The classification loss $L_{cls}$ is defined as:

L_{cls} = - \frac{1}{N_{s}} \sum_{i = 1}^{N_{s}} \sum_{c = 1}^{C} I (y_{i} = c) \log G_{y} {(f_{i})}_{c}

(24)

where $N_{s}$ is the number of source domain samples, $I$ is the indicator function, and $G_{y} {(f_{i})}_{c}$ indicates the predicted probability that the sample belongs to class $c$ .

In summary, the overall training objective of the proposed HDCAN method is to simultaneously minimize the fault classification error and cross-modal discrepancy, while maximizing the domain discrimination error. The total loss function, denoted as $L_{total}$ , is composed of three components: the source domain classification loss $L_{cls}$ , the cross-modal contrastive loss $L_{CL}$ , and the domain adversarial loss $L_{domain}$ :

L_{total} = L_{cls} + λ_{cont} L_{CL} + L_{domain}

(25)

where $λ_{cont}$ represents the weighting coefficient for the contrastive loss. HDCAN-based algorithm for cross-machine fault diagnosis is shown in Algorithm 1.

Algorithm 1.

HDCAN for cross-machine fault diagnosis.

1: Input:
2: Source labeled set

D_{s} = {(x_{seq}^{i}, y^{i})}_{i = 1}^{N_{s}}

with label space

Y_{s}

.
3: Target unlabeled set

D_{t} = {x_{seq}^{j}}_{j = 1}^{N_{t}}

.
4: Hyperparameters: batchsize

N

, learning rate

η

, temperature coefficient

τ

, weight coefficients

λ_{cont}, λ_{adv}

.
5: Output: Optimized HDCAN model

θ

and target predictions

{\hat{y}}_{t}

.
# Data Preprocessing Stage
6: Normalize signals

x_{seq}

using Z-score standardization.
7: Construct heterogeneous inputs: Use Equations (1)–(3) to transform

X_{seq} \to X_{img}

via SDP transformation.
# Training Stage (Offline)
8: Initialize network weights

θ

.
9: for

epoch = 1

epochs

do
10: Sample batch of size

N

from

D_{s}

and

D_{t}

.
11: Dual-Stream Extraction: Use Equations (8) and (12) to obtain

V_{img}

and

V_{signal}

.
12: Cross-Modal Alignment:
13: Use Equations (13) and (14) to project features to

Z_{img}, Z_{signal}

.
14: Use Equation (15) apply

L_{2}

normalization.
15: Use Equations (16) and (17) to calculate contrastive loss

L_{CL}

.
16: Feature Fusion:

F_{final} = Fusion ([V_{img} \oplus V_{signal}])

.
17: Calculate classification loss

L_{cls}

on source data.
18: Use Equation (23) to calculate domain adversarial Loss

L_{domain}

.
19: Optimization:
20: Use Equation (25) to calculate

L_{total}

L_{total} = L_{cls} + λ_{cont} L_{CL} + λ_{adv} L_{domain}

.
21: Update parameters

θ

via backpropagation:

θ \leftarrow θ - η \nabla_{θ} L_{total}

.
22: end for
# Test Stage (Online)
23: Input: Unseen target domain test set

D_{test}

.
24: Standardize signals and generate SDP images.
25: Forward propagation to obtain probability distribution and obtain predicted labels.
26: Output: Cross-machine fault diagnosis accuracy and classification report.

Diagnosis procedure

The fault diagnosis process of the proposed HDCAN is illustrated in Figure 9. It primarily comprises three distinct phases: data preprocessing, offline model training, and online cross-domain fault diagnosis. The detailed implementation steps are outlined as follows:

Phase 1: Data preprocessing and multi-modal construction: This phase aims to transform raw 1D vibration data into the heterogeneous input format required by the model, establishing signal-image paired samples. First, raw vibration signals from distinct mechanical equipment are acquired via accelerometers, with the source and target domains explicitly designated. A sliding window technique is employed to segment the continuous vibration signals into samples of length 1024, serving as the input $X_{seq}$ for the time-domain signal branch. Furthermore, the SDP method is utilized to map the aforementioned 1D time series onto a polar coordinate system. By calculating the mirror symmetry relationships between signal points, the corresponding 2D color images $X_{img}$ are generated. Ultimately, a heterogeneous dual-stream dataset is constructed, comprising the labeled source domain dataset $D_{s} = {(X_{seq}^{i}, X_{img}^{i}, y^{i})}_{i = 1}^{N_{s}}$ and the unlabeled target domain dataset $D_{t} = {(X_{seq}^{j}, X_{img}^{j})}_{j = 1}^{N_{t}}$ .

Phase 2: Offline training and joint adversarial optimization: This phase utilizes data from both the source and target domains to perform end-to-end offline training on the heterogeneous dual-stream adversarial network, aiming to learn domain-invariant discriminative fault features. First, the paired samples are fed into the network, where the image geometric features $V_{img}$ and temporal features $V_{signal}$ are extracted in parallel via the dual-stream architecture. Subsequently, the InfoNCE loss between $V_{img}$ and $V_{signal}$ is computed through the Cross-Modal Contrastive Learning module. By maximizing the mutual information between features of different modalities from the same sample, feature consistency is enhanced. The aligned features are then concatenated and fused into $F_{final}$ via a fully connected layer to facilitate both the fault classification and domain adversarial tasks. Finally, based on the total loss function $L_{total}$ , the model parameters are iteratively updated using the Adam optimizer, and the optimal parameters for the feature extractor and classifier are saved.

Phase 3: Online testing and cross-domain diagnosis. In the online testing phase, the well-trained model is deployed to perform real-time diagnosis on target equipment operating under novel working conditions. First, vibration monitoring data from the target equipment are acquired in real-time and converted into standardized signal segments $X_{seq}^{test}$ and corresponding SDP images $X_{img}^{test}$ . The processed heterogeneous data are then fed into the offline model with fixed parameters. The category with the maximum predicted probability is determined as the final fault diagnosis result, thereby achieving high-precision cross-device state identification under unsupervised conditions.

Figure 9.

Diagnosis procedure of HDCAN. HDCAN: heterogeneous dual-stream contrastive adversarial network.

Experimental study

This section validates the effectiveness of the proposed HDCAN method using public datasets from CWRU, JNU, and XJTU. It details the implementation process, training and testing procedures, and evaluation methods.

Dataset description

To validate the effectiveness of the proposed HDCAN in cross-machine diagnostic scenarios, we performed six transmission diagnostic tasks using bearing datasets obtained from three distinct yet related machines. Below are the details of the three datasets.

CWRU bearing dataset: Provided by CWRU,³⁹ this dataset is widely regarded as the benchmark for bearing diagnostics. The experimental platform, as shown in Figure 10, consists of a drive motor, a loading motor, a torque sensor, a dynamometer, and several test bearings. On the experimental platform, EDM was used to induce faults in motor bearings, creating defects with a diameter of 0.007 inches on the inner ring, rolling elements, and outer ring. Four load conditions were simulated: 0, 1, 2, and 3 hp. Vibration signals from the drive-end bearing were recorded at a sampling frequency of 12 kHz. Four healthy condition samples were selected: N, BF, IF, and OF.

JNU bearing dataset: Provided by JNU.⁴⁰ Figure 11 shows a schematic diagram of the rolling bearing fault diagnosis experimental setup. On the experimental platform, four fault types: N, BF, IF, and OF were artificially generated via wire cutting. Data for each fault type was recorded at three distinct rotational speeds: 6000, 8000, and 1000 rpm. Vibration signals from the drive-end bearing of the conveyor system were recorded at a sampling frequency of 50 kHz in the JNU dataset.

XJTU bearing dataset: Provided by the Aero Engine Research Institute of XJTU.⁴¹ The experimental platform, as shown in Figure 12, consists of a drive motor, controller, planetary gearbox, parallel gearbox, and brake. In the experiment, four types of bearing faults were simulated on the planetary gearbox, including normal operation, rolling element failure, inner ring failure, and outer ring failure. During the experiment, the motor speed was set to 1800 r/min, and the sampling frequency was set to 20,480 Hz.

Figure 10.

The test rig of CWRU bearing datasets. CWRU: Case Western Reserve University.

Figure 11.

The test rig of JNU bearing datasets. JNU: Jiangnan University.

Figure 12.

The test rig of XJTU bearing datasets. XJTU: Xi’an Jiaotong University.

Fault diagnostic tasks and implementation details

Each category in the source domain and target domain contains 1000 samples, resulting in 4000 samples for each domain. The training dataset includes both source domain and target domain samples, while the test dataset contains only target domain samples. Six cross-machine transfer tasks were constructed using the three distinct yet related bearing datasets to validate HDCAN’s effectiveness: A $\to$ B, B $\to$ A, A $\to$ C, C $\to$ A, B $\to$ C, and C $\to$ B. It is important to note that these six cross-machine transfer tasks collectively encompass both load and speed transfer. Taking A $\to$ B as an example, A and B represent the labeled source domain and the unlabeled target domain, respectively. Detailed information on the three datasets is shown in Table 1. Except for health status, all parameters of these datasets are mutually distinct. This indicates that the six transfer tasks represent different challenges for the HDCAN method in performing accurate diagnostics.

Table 1.

Detailed information of three datasets.

Name	Code	Bearing	Condition	Working condition
CWRU	A	Motor bearing	BF	0/1/2/3 hp 1797/1772 1750/1720 r/min
			IF
			OF
			N
JNU	B	Conveyor system bearing	BF	600/800/1000 r/min
			IF
			OF
			N
XJTU	C	Planetary gearbox bearing	BF	1800 r/min
			IF
			OF
			N

CWRU: Case Western Reserve University; JNU: Jiangnan University; XJTU: Xi’an Jiaotong University; N: Normal; BF: Ball Fault; IF: Inner Ring Fault; OF: Outer Ring Fault.

The experiments were conducted in a software environment utilizing TensorFlow 2.10.0 and the Python 3.8 programming language. The hardware environment comprised an NVIDIA GeForce RTX 4060 Ti GPU and an i7-1165G7 CPU. The base hyperparameters for the experimental method were determined based on relevant prior research and experimental requirements to achieve satisfactory performance. The sample length, batch size, learning rate, and number of epochs were set to 1024, 64, 0.001, and 100, respectively. The model architecture and corresponding parameter settings used in the experiments are presented in Table 2. To mitigate randomness, the experiments were repeated 10 times, and the average results are reported.

Table 2.

Detailed structural parameters of the proposed HDCAN architecture.

Component	Module	Operation	Parameter
Geometric-aware image branch	Feature backbone	EfficientNetB0	Pretrained (ImageNet)
	Geometric enhancement	Conv2D	$c = 64, k = 3, s = 1, p = same$
		BN and GELU	—
		Conv2D	$c = 128, k = 3, s = 1, p = same$
	Spatial attention	Conv2D	$c = 1, k = 1, s = 1, σ = Sigmoid$
	Pooling and fusion	GAP and GMP	—
		Concat	$d_{out} = 256$
		Dense	$d = 256$
Time-domain signal branch	Large scale	Conv1D	$c = 32, k = 64, s = 8, p = same$
	Large scale	Conv1D	$c = 64, k = 32, s = 1, p = same$
	Medium scale	Conv1D	$c = 32, k = 32, s = 4, p = same$
	Medium scale	Conv1D	$c = 64, k = 16, s = 1, p = same$
	Small scale	Conv1D	$c = 32, k = 16, s = 2, p = same$
	Small scale	Conv1D	$c = 64, k = 8, s = 1, p = same$
	Statistical features	Dense	$d_{in} = 10, d_{out} = 32$
	Adaptive fusion	Dense	$d = 128, Dropout = 0.2$
Cross-modal contrastive module	Image projection	Dense-LN	$d_{in} = 256, d_{out} = 128$
	Signal projection	Dense-LN	$d_{in} = 128, d_{out} = 128$
	Similarity	Dot product	$τ = 0.1, L_{2} - Norm$
	Loss function	InfoNCE	$λ_{cont} = 0.05$
Task learning and adversarial	Feature fusion	Concat and dense	$d_{in} = 384, d_{out} = 256$
	Fault classifier	Linear	$d = 4$ (Softmax)
	Domain discriminator	GRL	$λ_{adv} = 0.3$
	Domain discriminator	Linear	$d = 2$ (Softmax)

$c$ : output channels; $k$ : kernel size; $s$ : stride; $p$ : padding; $d$ : dimension of dense layer; $σ$ : activation function; HDCAN: heterogeneous dual-stream contrastive adversarial network; GRL: gradient reversal layer; GAP: global average pooling; GMP: global max pooling; BN: batch normalization; LN: layer normalization.

Building upon the fundamental settings described above, the optimal hyperparameter grid search results related to the model and experimental configurations are illustrated in Figure 13. As can be seen from the figure, the core parameters of the proposed method in this study are the contrastive learning weight $λ_{cont}$ and the domain adversarial weight $λ_{adv}$ . These two parameters synergistically regulate the intra-class compactness and the degree of cross-domain distribution alignment within the model’s feature space. To ensure the systematicity and comprehensiveness of the optimization, we adopted a Grid Search strategy, setting the search space for $λ_{cont}$ to ${0.01, 0.05, 0.1, 0.2, 0.5}$ and the search space for $λ_{adv}$ to ${0.1, 0.2, 0.3, 0.5, 0.7}$ . Further analysis of the parameter sensitivity trends reveals that extreme values for either single parameter lead to model performance degradation. On the one hand, when $λ_{adv}$ is at a lower level adversarial penalty is insufficient to effectively eliminate the distribution shift between the source and target domains. Conversely, when $λ_{adv}$ increases to 0.7, an excessively strong domain confusion penalty disrupts the original discriminative boundaries of the features, thereby triggering negative transfer. On the other hand, the contrastive weight $λ_{cont}$ similarly exhibits superior robustness at intermediate thresholds: an extremely small $λ_{cont}$ fails to effectively pull samples of the same class closer together, while an excessively large $λ_{cont}$ dominates the overall loss function, interfering with the normal convergence of the classifier. The final grid optimization results demonstrate that if and only if $λ_{cont}$ is 0.05 and $λ_{adv}$ is 0.3, the model achieves an optimal dynamic balance between feature discriminability and domain invariance, realizing the highest diagnostic accuracy of 95.04% on the target domain.

Figure 13.

Grid search for optimal core hyperparameters.

Comparison experiment

To further verify the effectiveness of HDCAN, nine state-of-the-art methods are adopted for comparison in six cross-domain fault diagnosis experiments. As shown in Table 3, the comparative methods include one non-domain adaptive method serving as the baseline (CNN), two methods based on signal-to-image conversion and information fusion (SDP-DRN⁴⁴ and PSDP-MLFNet⁴⁵), three metric-based methods (deep domain adaptation (DCORAL),⁴⁶ multiple feature spaces adaptation network (MFSAN),⁴⁷ and MSSA⁴⁸), as well as three adversarial learning-based methods (maximum classifier discrepancy (MCD),⁴⁹ adversarial cross-domain DANN (ACDANN),⁵⁰ and domain-adversarial neural networks (DANN)⁴⁶). In accordance with different comparison objectives, the nine methods are classified into four groups:

Comparison with domain-free adaptation methods. The first category demonstrates improvements achieved by domain-adaptive methods in transfer fault diagnosis tasks where target domain data is unlabeled. The comparison method involves training the CNN solely on source data, using only source domain data to train the CNN. Therefore, the CNN serves as the baseline for comparison with HDCAN methods.

Comparison with signal-to-image and multimodal fusion methods. The second category evaluates the effectiveness of converting original time series signals into spatial image patterns and fusing multi-channel information for fault identification. The SDP-DRN method adopts the SDP technique to transform 1D time series data into 2D snowflake graphs, and employs a deep residual network (DRN) for robust pattern diagnosis. PSDP-MLFNet further extends this research idea. It utilizes pixelated SDP (PSDP) to realize multi-channel fusion at the data level, and combines a multi-level fusion network (MLFNet) to jointly extract and exploit multimodal features from generated images and original vibration signals.

Comparison with metric-based DA methods. The third category illustrates how metric-based approaches utilize statistical distances to measure similarity between source and target domains, thereby assessing the impact of distributional differences. DCORAL aligns source and target domain distributions via the correlation alignment (CORAL) metric. MFSAN adjusts the distributions for each source-target domain pair and calibrates classifier outputs using domain-specific decision boundaries, selecting MMD to estimate differences between domains. MSSA employs a multi-branch network architecture and utilizes local MMD (LMMD) for subdomain distribution alignment.

Comparison with the latest domain-based adversarial methods. The fourth category compares three widely used domain adversarial methods to demonstrate that adversarial approaches designed between feature extractors and domain discriminators can implicitly guide domain-invariant features. MCD aligns source and target distributions using task-specific decision boundaries. ACDANN integrates expert knowledge into fault diagnosis models through synthetic dataset generation and DA techniques. DANN incorporates a domain-discriminative component into deep neural networks, aiming to learn feature representations that are predictive on source domain data samples while insensitive to input domain information.

Table 3.

Detailed comparison of different fault diagnosis methods.

Method	Category	Alignment strategy/core mechanism
CNN	Domain-free baseline	Source-only training without adaptation
SDP-DRN⁴⁴	Information fusion	Transforming 1D signals into 2D SDP snowflakemaps coupled with DRN
PSDP-MLFNet⁴⁵	Information fusion	Multi-channel fusion via PSDP and multi-modal feature learning using MLFNet
DCORAL⁴⁶	Metric-based	Minimizing second-order statistics via CORAL
MFSAN⁴⁷	Metric-based	Aligning domain-specific decision boundaries via MMD
MSSA⁴⁸	Metric-based	Subdomain alignment using LMMD
MCD⁴⁹	Adversarial-based	Adversarial training via maximizing task-specific decision boundary discrepancy
ACDANN⁵⁰	Adversarial-based	Integrating expert knowledge with DANN
DANN⁴⁶	Adversarial-based	Domain discrimination via GRL
HDCAN (Ours)	Adversarial-based	Cross-Modal Contrastive Alignment and Domain Adversarial Adaptation

CNN: convolutional neural network; SDP: symmetric dot pattern; DRN: deep residual network; PSDP: pixelated symmetric dot pattern; MLFNet: multi-level fusion network; DANN: domain-adversarial neural networks; HDCAN: heterogeneous dual-stream contrastive adversarial network; 1D: one-dimensional; 2D: two-dimensional; MMD: maximum mean discrepancy; LMMD: local maximum mean discrepancy; GRL: gradient reversal layer.

Cross-machine diagnostic performance

Three statistical criteria, that is, accuracy (ACC), precision (PRE), and F1-score (F1) on six cross-machine tasks were used to evaluate the diagnostic results, as listed in Table 4 and Figure 14. The experimental results show that the proposed HDCAN method achieves the best overall performance, with average accuracy, accuracy, and F1 score reaching 92.10, 92.08, and 90.93%, respectively. Specifically, in the C $\to$ B transfer task, the CNN’s accuracy significantly dropped to 38.87%, revealing substantial feature distribution variation between the source and target domains that the baseline model struggled to handle. In contrast, the HDCAN method achieved a 35.58% improvement in average accuracy over CNN, validating its effectiveness in extracting domain-invariant features for cross-machine fault diagnosis. Furthermore, signal-to-image and multimodal fusion methods (SDP-DRN and PSDP-MLFNet) achieve average accuracies of only 64.29 and 66.95%, respectively. Although they outperform the baseline CNN in feature extraction via image conversion and multi-channel fusion, they lack explicit feature alignment mechanisms. Consequently, mere data-level transformations cannot effectively eliminate significant cross-machine distribution shifts, leaving their knowledge transferability severely limited. Furthermore, when compared with metric-based TL approaches, MSSA achieved certain performance gains through its multi-branch architecture and LMMD. However, it primarily relies on edge distributions or conditional distributions from MMD and CORAL, limiting its capability in handling complex feature shifts such as cross-device processing. In contrast, the HDCAN method introduces a dual-stream contrastive learning strategy that effectively compresses the distance between similar samples in the feature space. This enables robust fault recognition capabilities even when confronted with varying operating conditions across different mechanical equipment. Furthermore, unlike adversarial methods such as MCD and ACDANN that focus solely on category information and single fault features, the HDCAN method integrates pattern features from SDP images with time-domain features from raw vibration signals. This multi-view mechanism captures richer fault representations, achieving not only the highest accuracy of 98.75% in the B $\to$ C task but also demonstrating exceptional stability and generalization across all transfer scenarios. This fully validates HDCAN’s superiority in addressing fault diagnosis across complex mechanical equipment.

Table 4.

Diagnosis results on cross-machine tasks.

Methods	Evaluation criteria	A → B	A →C	B→ A	B → C	C→A	C →B	Average
CNN	ACC (%)	$52.74 \pm 8.61$	$75.15 \pm 0.37$	$46.79 \pm 10.68$	$67.21 \pm 17.91$	$58.36 \pm 1.01$	$38.87 \pm 4.48$	$56.52 \pm 7.18$
	PRE (%)	$52.96 \pm 9.30$	$73.74 \pm 0.36$	$42.84 \pm 13.87$	$53.29 \pm 23.09$	$60.23 \pm 0.92$	$39.99 \pm 5.83$	$53.84 \pm 8.90$
	F1 (%)	$49.75 \pm 11.54$	$75.15 \pm 0.37$	$46.79 \pm 13.90$	$67.21 \pm 23.36$	$61.71 \pm 1.01$	$38.87 \pm 6.47$	$56.58 \pm 9.44$
SDP-DRN	ACC (%)	$62.83 \pm 2.19$	$67.32 \pm 7.35$	$55.09 \pm 0.85$	$66.99 \pm 4.10$	$87.69 \pm 2.48$	$54.39 \pm 3.94$	$64.29 \pm 3.49$
	PRE (%)	$64.78 \pm 0.96$	$69.09 \pm 5.34$	$55.21 \pm 0.76$	$62.47 \pm 1.99$	$87.91 \pm 2.29$	$54.63 \pm 5.66$	$64.49 \pm 2.83$
	F1 (%)	$62.22 \pm 3.24$	$65.63 \pm 8.26$	$55.08 \pm 0.85$	$69.09 \pm 4.91$	$87.43 \pm 2.43$	$52.00 \pm 5.18$	$63.58 \pm 4.15$
PSDP-MLFNet	ACC (%)	$45.98 \pm 3.05$	$77.55 \pm 7.29$	$62.35 \pm 10.51$	$79.63 \pm 0.39$	$75.10 \pm 4.86$	$76.82 \pm 1.17$	$66.95 \pm 4.55$
	PRE (%)	$37.19 \pm 5.60$	$71.94 \pm 3.30$	$67.22 \pm 15.26$	$72.03 \pm 3.51$	$77.47 \pm 11.59$	$74.18 \pm 4.90$	$63.10 \pm 7.36$
	F1 (%)	$33.47 \pm 2.49$	$72.62 \pm 6.73$	$61.87 \pm 13.79$	$77.17 \pm 2.30$	$76.29 \pm 7.65$	$77.09 \pm 4.25$	$63.09 \pm 6.20$
DCORAL	ACC (%)	$72.40 \pm 8.40$	$68.93 \pm 3.58$	$79.46 \pm 0.15$	$62.41 \pm 6.42$	$74.62 \pm 4.67$	$80.83 \pm 2.50$	$73.11 \pm 4.29$
	PRE (%)	$72.54 \pm 7.92$	$69.29 \pm 3.27$	$79.32 \pm 0.13$	$62.98 \pm 5.61$	$74.51 \pm 5.30$	$82.05 \pm 2.20$	$73.45 \pm 4.07$
	F1 (%)	$72.26 \pm 8.34$	$69.71 \pm 3.29$	$78.96 \pm 0.15$	$62.56 \pm 3.77$	$75.14 \pm 4.77$	$80.83 \pm 2.52$	$73.24 \pm 3.81$
MFSAN	ACC (%)	$48.33 \pm 10.18$	$75.83 \pm 11.20$	$48.83 \pm 6.65$	$74.62 \pm 1.34$	$56.50 \pm 17.63$	$62.42 \pm 9.37$	$61.01 \pm 9.40$
	PRE (%)	$59.73 \pm 4.57$	$78.14 \pm 12.73$	$45.54 \pm 4.03$	$74.51 \pm 1.19$	$69.77 \pm 20.51$	$73.36 \pm 16.00$	$66.84 \pm 9.84$
	F1 (%)	$47.39 \pm 7.99$	$70.79 \pm 12.49$	$44.58 \pm 7.49$	$75.14 \pm 1.35$	$46.85 \pm 18.91$	$62.64 \pm 12.28$	$57.90 \pm 10.09$
MSSA	ACC (%)	$80.20 \pm 4.29$	$79.77 \pm 1.48$	$78.34 \pm 1.27$	$85.36 \pm 2.09$	$75.26 \pm 3.41$	$72.68 \pm 5.82$	$78.59 \pm 3.06$
	PRE (%)	$80.61 \pm 3.17$	$79.77 \pm 1.33$	$78.42 \pm 1.16$	$85.61 \pm 1.93$	$79.31 \pm 4.32$	$77.44 \pm 3.64$	$80.18 \pm 2.59$
	F1 (%)	$79.95 \pm 4.06$	$76.86 \pm 1.51$	$78.34 \pm 1.29$	$85.26 \pm 2.16$	$75.34 \pm 3.82$	$71.48 \pm 5.80$	$77.86 \pm 3.11$
MCD	ACC (%)	$76.60 \pm 5.60$	$80.23 \pm 7.45$	$88.97 \pm 3.04$	$89.41 \pm 9.73$	$63.53 \pm 1.57$	$57.79 \pm 3.24$	$76.20 \pm 5.11$
	PRE (%)	$83.12 \pm 2.99$	$78.15 \pm 5.17$	$89.41 \pm 5.57$	$87.52 \pm 12.08$	$51.24 \pm 1.51$	$58.47 \pm 5.55$	$76.15 \pm 5.48$
	F1 (%)	$77.20 \pm 4.89$	$79.54 \pm 6.04$	$88.72 \pm 0.19$	$87.17 \pm 10.86$	$56.19 \pm 1.73$	$55.15 \pm 4.70$	$73.11 \pm 4.74$
ACDANN	ACC (%)	$75.61 \pm 3.90$	$86.34 \pm 0.72$	$88.66 \pm 2.16$	$93.76 \pm 4.70$	$89.65 \pm 5.32$	$76.26 \pm 3.57$	$85.05 \pm 3.40$
	PRE (%)	$77.90 \pm 5.24$	$86.81 \pm 0.67$	$90.43 \pm 1.97$	$94.01 \pm 3.99$	$89.41 \pm 4.27$	$77.48 \pm 4.83$	$86.01 \pm 3.50$
	F1 (%)	$76.02 \pm 3.67$	$86.32 \pm 0.72$	$87.67 \pm 2.18$	$93.74 \pm 4.92$	$88.32 \pm 4.63$	$76.18 \pm 3.44$	$84.71 \pm 3.26$
DANN	ACC (%)	$74.39 \pm 8.00$	$76.37 \pm 0.09$	$64.23 \pm 0.35$	$72.39 \pm 1.27$	$74.57 \pm 10.22$	$73.16 \pm 10.41$	$72.52 \pm 5.06$
	PRE (%)	$75.38 \pm 3.78$	$74.13 \pm 0.15$	$64.89 \pm 0.33$	$74.21 \pm 1.17$	$76.23 \pm 5.05$	$80.28 \pm 4.16$	$74.19 \pm 2.43$
	F1 (%)	$74.22 \pm 7.59$	$74.82 \pm 0.08$	$66.74 \pm 0.35$	$73.66 \pm 1.30$	$74.81 \pm 13.36$	$70.58 \pm 11.02$	$72.47 \pm 5.62$
Proposed	ACC (%)	$89.93 \pm 1.17$	$96.63 \pm 0.16$	$95.13 \pm 1.21$	$98.75 \pm 0.14$	$88.56 \pm 2.31$	$83.62 \pm 1.08$	$92.10 \pm 1.01$
	PRE (%)	$90.33 \pm 1.20$	$97.05 \pm 0.16$	$93.04 \pm 1.22$	$98.23 \pm 0.16$	$88.75 \pm 2.69$	$85.08 \pm 1.06$	$92.08 \pm 1.08$
	F1 (%)	$89.60 \pm 1.19$	$96.63 \pm 0.17$	$95.74 \pm 1.19$	$97.25 \pm 0.15$	$83.21 \pm 2.44$	$83.12 \pm 1.11$	$90.93 \pm 1.04$

Figure 14.

Cross-machine diagnostic performance of different methods: (a) accuracy, (b) precision, and (c) F1 score.

Class-specific diagnostic performance

To investigate the identification capability for specific fault categories, a confusion matrix is employed to demonstrate the diagnostic accuracy for each category. Figure 15 presents the average results for the A $\to$ B task by comparing nine competing methods. First, the rows and columns in the confusion matrix represent actual and predicted labels, respectively. Second, the values along the main diagonal represent the correct classification rates for each health state. It is observed that the proposed HDCAN achieves high diagnostic accuracy for every fault category, particularly reaching 99% accuracy for normal samples. In the CNN method confusion matrix, BF, IF, and N samples exhibit significant misclassification, indicating limited knowledge transferability in CNN cross-domain fault diagnosis. Furthermore, while signal-to-image and multimodal fusion method PSDP-MLFNet perform well on OF (93%) and N (92%) states, they fail severely on BF (6%) and IF (2%) states, frequently misclassifying these samples as OF or N. This indicates that without explicit DA, mere image conversion and feature fusion cannot effectively overcome significant cross-machine distribution shifts. Notably, samples in the OF state are frequently misclassified as BF or N states. Taking the MFSAN method as an example, only 40% of OF samples are successfully identified, while the remaining 58% are misdiagnosed as N states. In contrast, HDCAN achieves an accuracy of 95%, demonstrating its robust diagnostic performance for difficult-to-distinguish fault samples. Furthermore, compared to other domain-based adversarial methods like ACDANN and DANN, the proposed HDCAN method significantly improves misclassified fault diagnosis results. This demonstrates that HDCAN learns optimal domain-invariant and class-separating features through its dual-stream contrastive strategy.

Figure 15.

Confusion matrix of nine methods: (a) CNN, (b) SDP-DRN, (c) PSDP-MLFNet, (d) MFSAN, (e) MSSA, (f) MCD, (g) ACDANN, (h) DANN, and (i) HDCAN (Ours). CNN: convolutional neural network; SDP: symmetric dot pattern; DRN: deep residual network; PSDP: pixelated symmetric dot pattern; MLFNet: multi-level fusion network; DANN: domain-adversarial neural networks; HDCAN: heterogeneous dual-stream contrastive adversarial network.

Fault feature visualization

To intuitively evaluate the performance of each model in feature extraction and cross-domain alignment, we employed t-distributed Stochastic Neighbor Embedding to visualize the extracted features in task B $\to$ C. Several methods, including CNN, SDP-DRN, DCORAL, MSSA, MCD, ACDANN, DANN, and the proposed HDCAN method, were selected for comparison, with the results presented in Figure 16. In the visualization, different colors denote different health states (BF, IF, OF, N), while circles and stars represent source domain samples and target domain samples, respectively. As observed from the feature distribution of the CNN model (trained solely on the source domain), although different fault categories exhibit a certain trend of separation, there is a significant discrepancy between source and target domain features within the same health state. This indicates that without DA strategies, the model is severely affected by domain shift, making it difficult to extract generalized features suitable for the target domain. As a method for converting 1D time-series signals into 2D snowflake plots for spatial pattern recognition, SDP-DRN exhibits highly divergent feature distributions, with significant distribution discrepancies between the source and target domains for similar samples. Furthermore, while DANN and DCORAL reduce the distribution distance between source and target domains compared to CNN, obvious overlapping still exists between different fault categories, between IF and OF samples. The cluster boundaries remain ambiguous, suggesting that DANN and DCORAL are insufficient in extracting highly discriminative features. It is worth noting that MCD and ACDANN perform relatively better, with improved clustering compactness for samples of the same class. However, scattered samples and non-compact cluster centers are still observed in certain categories, indicating that their feature alignment capabilities under complex working conditions remain limited. In contrast, the proposed HDCAN method demonstrates the most superior feature clustering performance. The four health states are clearly separated into four distinct clusters with significant margins between them. Moreover, within each fault category cluster, the source and target domain samples overlap highly. These experimental results demonstrate that the HDCAN method effectively mines deep domain-invariant features and significantly minimizes the distribution discrepancy between source and target domains, thereby successfully overcoming distribution shifts across different mechanical data.

Figure 16.

Feature visualization for eight methods: (a) CNN, (b) SDP-DRN, (c) DCORAL, (d) MSSA, (e) MCD, (f) ACDANN, (g) DANN, and (h) HDCAN (Ours). CNN: convolutional neural network; SDP: symmetric dot pattern; DRN: deep residual network; DANN: domain-adversarial neural networks; HDCAN: heterogeneous dual-stream contrastive adversarial network.

Generalization performance across operating conditions

To comprehensively evaluate the transfer robustness and overall generalization capability of the proposed method under varying operating conditions, a radar chart and its corresponding metric, the volume of hexagonal area (VHA), are introduced, as illustrated in Figure 17. The enclosed area of the radar chart intuitively reflects the balance of the model’s overall performance across the six cross-domain tasks; a larger VHA value indicates superior adaptability in handling complex variable operating conditions. Experimental results demonstrate that the proposed HDCAN method exhibits optimal cross-domain generalization performance, achieving a VHA value of $22.06 \times 10^{3}$ . This figure significantly outperforms the deep adversarial network DANN ( $13.65 \times 10^{3}$ ) and the DA baseline DCORAL ( $13.84 \times 10^{3}$ ), while also surpassing advanced methods such as ACDANN ( $18.83 \times 10^{3}$ ) and MSSA ( $16.04 \times 10^{3}$ ). In contrast, the CNN model, lacking any transfer strategy, yields a VHA value of only $8.16 \times 10^{3}$ . Its radar chart displays a distinct contracted morphology, indicating that standard deep learning models struggle to cope with the severe feature distribution shifts induced by cross-device operating conditions. Furthermore, signal-to-image and multimodal fusion methods SDP-DRN and PSDP-MLFNet achieve VHA values of $7.73 \times 10^{3}$ and $6.80 \times 10^{3}$ , respectively. This further indicates that under complex variable operating conditions, without an explicit DA strategy, merely transforming 1D time-series data into 2D spatial image patterns not only fails to overcome cross-machine feature distribution shifts but may also lead to a further decline in generalization performance due to the inability to effectively align cross-domain features during the transformation process. In summary, the substantial VHA value, combined with superior performance across individual tasks, collectively validates the effectiveness of HDCAN. By employing distinct architectures for feature extraction and leveraging cross-modal contrastive learning, the proposed method not only effectively overcomes feature heterogeneity across different operating conditions but also achieves a balanced performance distribution while maintaining high accuracy. These results underscore its significant potential for practical applications in industrial cross-machine fault diagnosis scenarios.

Figure 17.

Radar maps for cross-domain diagnosis tasks.

Synergy analysis of optimization objectives

In this section, an in-depth collaborative analysis is conducted on the joint optimization process involving gradient conflict as well as the feature discriminative ability of cross-modal alignment and domain adversarial learning. The optimization objectives of cross-modal contrastive learning and domain adversarial learning present prominent synergistic effects. On the one hand, the domain adversarial module is designed to minimize the marginal distribution discrepancy between the source domain and target domain to realize distribution-level alignment. On the other hand, as illustrated in Equation (17), the cross-modal contrastive loss $L_{CL}$ can extract modal-invariant features by maximizing the similarity of positive sample pairs. Meanwhile, the negative sample penalty mechanism embedded in its denominator enables the model to effectively separate feature samples of different fault categories in the feature space.

To intuitively verify the synergistic effect of the above optimization objectives, Figure 18 presents the changing trends of various losses and target domain accuracy during the training of HDCAN in the B $\to$ A cross-machine diagnosis task. In Figure 18(a), to accurately evaluate the domain alignment effect in the adversarial network, the Domain Discrepancy Gap is introduced for normalized visualization, and its definition is shown as follows: $2 \times | {Acc}_{domain} - 0.5 |$ . It can be observed from Figure 18(a) that in the initial training stage, restricted by the inherent physical distribution difference between the source domain and target domain, the domain classifier rapidly captures inter-domain deviations, leading to a temporary increase in the domain discrepancy gap. Nevertheless, under the combined action of GRL and cross-modal contrastive loss, the classification loss ( $L_{cls}$ ), cross-modal contrastive loss ( $L_{CL}$ ), and domain discrepancy gap show a highly synchronous and smooth downward trend. This demonstrates that modal alignment and domain adversarial alignment maintain high consistency and coordination in the direction of gradient update. Moreover, Figure 18(b) reveals that with the synergistic reduction of weighted total loss, the diagnosis accuracy of the model on the target domain rises steadily and eventually converges to a high-precision state. Consequently, the joint optimization strategy proposed in this work can effectively eliminate cross-domain distribution bias, and meanwhile preserve and strengthen the discriminative ability of extracted features, which fully verifies its synergistic validity.

Figure 18.

Training dynamics of the joint optimization process: convergence analysis and target domain accuracy: (a) normalized convergence trends and (b) total weighted loss versus target accuracy.

Ablation experiment

To comprehensively validate the effectiveness and necessity of the core innovative modules within the HDCAN framework for cross-machine fault diagnosis tasks, a systematic ablation analysis was conducted. This analysis was performed under identical experimental settings across six cross-domain tasks constructed from three distinct mechanical devices. By sequentially removing key components of the model, four variant models were established for comparative analysis:

HDCAN w/o CL (without cross-modal contrastive learning): This variant retains the dual-stream feature extraction and domain adversarial modules but eliminates the cross-modal contrastive learning loss $L_{CL}$ . It is designed to verify the necessity of heterogeneous feature alignment.

HDCAN w/o DA (without domain adversarial adaptation): While preserving dual-stream feature extraction and contrastive learning, this variant removes the GRL and the domain discriminator ( $L_{domain}$ ). Consequently, the model degrades into a version trained solely on the source domain and tested directly on the target domain. This setup serves to validate the efficacy of the adversarial strategy in suppressing domain shifts.

HDCAN w/o Image (without geometric-aware image branch): By removing the SDP image branch, the model degenerates into a single-modal time-domain network. This variant is employed to evaluate the specific contribution of SDP-based geometric visual information to fault representation.

HDCAN w/o Stat (without statistical feature fusion): This variant excludes the calculation and fusion of physical statistical features within the time-domain signal branch, relying exclusively on the multi-scale convolutional network. It aims to verify the auxiliary role of physical prior knowledge.

The detailed results of the ablation study are presented in Table 5. Overall, the complete HDCAN model achieves the optimal average accuracy (92.10%) across cross-domain tasks. In comparison, removing any individual component leads to significant performance degradation. First, the performance of HDCAN w/o DA exhibits the most drastic decline, with the average accuracy decreasing by 17.63%. This result indicates that in cross-device diagnosis scenarios, the domain shift in feature distribution caused by discrepancies in physical structures and operating conditions across different machines is the primary factor limiting the model’s generalization capability. Lacking the constraints of the domain adversarial module, the model struggles to learn domain-invariant features, resulting in a substantial weakening of diagnostic capability on the target domain. Secondly, the removal of the cross-modal contrastive learning module results in a significant 13.52% drop in the model’s diagnostic accuracy. This experimental result demonstrates that the absence of an effective modality alignment mechanism leads to the entanglement of heterogeneous features within the shared feature space, thereby triggering severe negative transfer. Conversely, the introduction of CL not only achieves precise modality alignment for heterogeneous features but also establishes a strong synergistic effect with the subsequent domain adversarial mechanism. Operating in tandem, they effectively decouple the interference caused by modality heterogeneity, thereby fundamentally averting the occurrence of negative transfer. Furthermore, the accuracy of HDCAN w/o Image and HDCAN w/o Stat declines by 11.07 and 9.87%, respectively. This suggests that relying solely on raw vibration signals is insufficient to comprehensively capture complex fault patterns. The structural information provided by SDP images, combined with the physical impact priors offered by statistical features, provides critical complementary perspectives for the deep neural network, effectively enhancing feature discriminability.

Table 5.

Average ablation results of six cross-machine tasks.

Model variant	Geometric image branch	Signal branch (MS-CNN)	Statistical features	Contrastive learning	Adversarial adaptation	Evaluation criteria accuracy (%)
HDCAN (Proposed)	✓	✓	✓	✓	✓	$92.10 \pm 1.01$
HDCAN w/o CL	✓	✓	✓	×	✓	$↓ 13.52 \pm 5.31$
HDCAN w/o DA	✓	✓	✓	✓	×	$↓ 17.63 \pm 3.57$
HDCAN w/o Image	×	✓	✓	×	✓	$↓ 11.07 \pm 4.70$
HDCAN w/o Stat	✓	✓	×	✓	✓	$↓ 9.87 \pm 3.91$

HDCAN: heterogeneous dual-stream contrastive adversarial network; CL: cross-modal contrastive learning; DA: domain adaptation; MS-CNN: multi-scale convolutional.

In summary, the superior performance of HDCAN is attributed to the synergistic interaction among heterogeneous dual-stream extraction, cross-modal feature alignment, and domain adversarial adaptation. These components collectively ensure the model’s robustness and high precision in complex cross-machine scenarios.

Conclusion

This paper proposed a HDCAN to tackle the domain shift issue in cross-machine mechanical fault diagnosis. The proposed method constructs heterogeneous multimodal inputs of signals and images via the SDP. It designs a geometry-aware image branch and a time-domain signal branch to extract features in parallel. Cross-modal contrastive learning is adopted to achieve the alignment of heterogeneous features, and a domain adversarial module is integrated to learn general features with both fault discriminability and domain invariance. The superiority and effectiveness of the method are validated on six cross-machine diagnosis tasks covering three types of mechanical equipment.

Although the proposed method can effectively alleviate the cross-machine domain shift problem, it still has certain limitations. For instance, it fails to fully consider the prevalent class imbalance issue in practical industrial scenarios, that is, the model performance in dealing with long-tailed distribution or minority-class fault samples remains to be further verified. In future work, we will focus on cross-machine fault diagnosis technology under class imbalance conditions and commit to lightweight model design, so as to promote the deployment and application of the model in practical engineering scenarios.

Footnotes

ORCID iD

Delong Cui

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to express their sincere thanks to the National Natural Science Foundation of China under grant 2273109 and grant 073090; in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2023A1515240020, under grant 2022A1515012022; and in part by the Sci-Tech Innovation Project of Graduate School of Guangdong University of Petrochemical Technology and the Sci-Tech Innovation Project for Joint Training Base of Professional Degrees Graduate of Maoming Green Chemical Industry Research Institute under grant 2025KJCX002, grant 2024KJCX006, grant 2024KJCX024, grant 2024KJCX032, and grant 2024KJCX002.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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