Study on fault sensitivity and feature distribution characteristics of wind turbine condition monitoring data

Abstract

This study investigates diagnostic index distribution patterns across fault modes in wind turbines (WTs) to establish boundary conditions for condition monitoring models. Using historical operational monitoring data and maintenance logs, we analyze the failure statistics of key WT components, the distribution of critical diagnostic indexes under fault conditions, and the co-occurrence relationships among various indexes across different operational states. We further investigate the power characteristics of generators and turbines under different fault modes, examining the fault sensitivity of diagnostic indexes and their mapping relationships with power output and wind speed. Finally, we propose a novel adaptive deep learning network model to validate the identified patterns, demonstrating its effectiveness in fault prediction under varying operational conditions. The results show more positive correlations between monitoring indexes at partial load during faults compared to rated power. The proposed adaptive deep learning model Adaptive Recurrent Neural Network (AdaRNN) reduces prediction errors to <5% by dynamically aligning temporal distributions through its temporal distribution characterization and matching framework. These findings enable prioritized monitoring of critical index correlations during high-power operation and wind-speed-specific modeling, providing field-ready solutions for predictive maintenance of WTs and similar mechanical systems.

Keywords

Condition monitoring fault sensitivity adaptive learning wind turbine temporal distribution

Introduction

In recent years, the rapid growth of wind power capacity, particularly in offshore installations, a transition toward larger and more intelligent structures has increasing the demands on operation and maintenance.¹ Although data-driven artificial intelligence enables real-time monitoring, fundamental gaps persist in characterizing fault behaviors under variable operational conditions. This has led to the gradual establishment of a scientific framework for monitoring the operational status of wind turbines (WTs), along with a categorization of related research fields. As data-driven artificial intelligence algorithms are increasingly applied in WT operational status monitoring, research focus is shifting from traditional offline testing and analysis to real-time online monitoring based on data-driven algorithms. This evolution signifies a pivotal advancement in the methodologies employed for ensuring the reliability and efficiency of wind energy systems.² At present, numerous scholars have incorporated data samples gathered from existing monitoring systems, such as Supervisory Control and Data Acquisition (SCADA) and condition monitoring systems (CMSs). Extensive research tackles WT reliability challenges, emphasizing early fault detection and health management—especially for failures with weak or gradual symptoms. By conducting statistical analyses, researchers monitor the operational status of relevant equipment or components, integrating engineering expertise to inform predictive maintenance strategies.^3,4

Data-driven operational status monitoring and fault diagnosis of WTs serves as a crucial method for real-time assessment of operational performance throughout the WT’s service life. This process can be broadly delineated into several sequential stages: data acquisition, data preprocessing, feature extraction, model construction and training, and ultimately, operational state prediction.⁵ The primary methods employed include residual signal-based information fusion,⁶ graph neural networks for physical-statistical features,⁷ cloud computing architectures,⁸ deep residual networks,^9,10 and cyclo-stationary models.¹¹ These techniques aim to enhance the early warning systems for potential failures and predict the remaining useful life of WTs.¹² Thereinto, ensuring data quality is crucial in data preprocessing, serving as a foundation for reliable monitoring and analysis. To address this, researchers have explored diverse methods to enhance the quality of WT monitoring data.¹³ These approaches include color space transformation and image feature detection, identifying anomalies in power curves based on operational mechanisms,¹⁴ utilizing feature pyramid networks to prevent feature confusion between low-quality and faulty data,¹⁵ and applying generative models for data augmentation to ensure temporal consistency.¹⁶

The precision of feature extraction from monitoring data directly impacts the false-positive and false-negative rates of predictive models in practical engineering applications. In response, many a study have been addressed on fault feature extraction in WTs, employing methods such as adaptive neuro-fuzzy inference systems,¹⁷ correlation analysis integrated with support vector machines,¹⁸ degradation feature fusion models,¹⁹ spatio-temporal feature extraction through mutual information accumulation,²⁰ and information flow fusion with supervised learning.²¹ These studies, validated through empirical data, have provided significant insights that may guide the establishment of failure thresholds and improve the accuracy of predictive models in early fault warning systems for critical WT components.

The safety performance analysis of WTs is challenging due to their complex structures and the dynamically changing external environment. With the rapid advancement of deep learning, intelligent monitoring research has gained momentum and positioned itself at the forefront of potential fault detection. In recent years, the development of data-driven artificial intelligence has facilitated the application of various machine learning algorithms in detecting potential faults in WTs. The majority of studies utilize methods such as temporal pattern attention mechanisms, dynamic kernel principal component mahalanobis distance,²² semi-supervised learning,²³ random forests,²⁴ gray relational analysis,²⁵ and deep learning.²⁶ These methods combine patterns in sensor data from critical WT components with machine learning to improve fault diagnostics. Nonetheless, most existing studies rely on data collected in controlled laboratory environments or pre-processed monitoring data, which limits model performance in the actual complex situation.²⁷ Meanwhile, the nonlinear relationships among monitoring indexes are intricate, primarily due to interactions involving time-varying wind speeds and multiple subsystems. To improve prediction model accuracy, classification reliability, and robustness, researchers have increasingly shifted their focus to integrating multi-parameter and multi-theory approaches for monitoring WT operational status. For example, the Bayesian theory and hypothesis testing,²⁸ deep joint autoencoders,²⁹ and attention modules coupled with gated cycle units.³⁰ Which predominantly emphasizes offline data processing, such as data cleaning, feature enhancement, and fusion informed by physical properties, as well as image segmentation and other theoretical frameworks.

The above studies have generated a wealth of conclusions regarding the regression predictions of essential monitoring indexes in WTs, providing valuable references for condition monitoring. However, a narrow focus on regression predictions for one or a few specific indexes complicates the integration of results, thereby impeding real-time assessments of the operational status of critical turbine components. In response, an alternative body of research has emerged, focusing on the application of machine learning-based classification tasks for the health monitoring and diagnosis of WTs and their critical components. Systems that utilize machine learning classification techniques for condition monitoring and warning predominantly concentrate on adversarial networks,^31,32 spectral ensemble sparse classification methods,³³ deep learning-based fuzzy synthesis,³⁴ neighborhood comparisons, clustering, decision trees, and K-nearest neighbors.^35,36 This emphasis arises from the need for fault feature analysis and the cointegration of monitoring data to tackle fault diagnosis, nonlinear data trends, and anomaly detection.³⁷ However, practical engineering datasets often contain limited instances of fault characteristics. As a result, several studies have adopted transfer learning algorithms, including parameter transfer and convolutional autoencoders,^38,39 feature transfer,⁴⁰ adversarial transfer learning,⁴¹ random forests,⁴² and deep domain adaptation.⁴³ These investigations target areas such as WT fault diagnosis, wind power forecasting, image recognition of WT blades, and health monitoring, validated against actual operational data from wind farms. The findings hold significant importance in mitigating data distribution discrepancies between different WTs, thereby enhancing the generalizability of monitoring data samples.

While existing studies focus on regression prediction under normal conditions, they overlook co-occurrence patterns among monitoring indexes across power ranges and the quantitative impacts of power characteristics on fault sensitivity. To address this gap, this study first analyzes 5-year failure statistics of components in doubly fed WTs across seven wind farms. Next, it investigates distribution characteristics and co-occurrences of monitoring index data from representative onshore and offshore WTs under typical fault modes, identifying indexes exhibiting distinct fault characteristics. Building on these foundations, the research then examines generator/overall power output variations and fault sensitivity of selected monitoring indexes under different fault modes. Finally, an adaptive deep learning algorithm validates the distribution characteristics of these selected indicators using historical data samples.

The rest of this paper is organized as follows. The second section elaborates on the process of distribution characteristics and fault sensitivity analysis, the process of temporal distribution characterization (TDC) and matching, and how to construct and verify the prediction model. The third section analyses the failure rate of each component and data distribution characteristics of each monitoring index under various operational statuses, and the fourth part is about the power characteristics, fault sensitivity of each monitoring index, and verification of the data distribution. The study has been addressed, combined with the data collected from Pandaoliang and Baxianjiao wind farms in Shanxi and Jiangsu, China.

The process of distribution characteristics and fault sensitivity analysis

This section focuses on the statistical analysis of failures in critical components of WT generators, particularly the data distribution characteristics and co-occurrence of various monitoring indexes under different fault conditions. It also examines the influence of different faults on the power characteristics of WTs and generators, as well as the fault sensitivity of each monitoring index. Finally, it validates the identified patterns, as illustrated in Figure 1. The research content can be divided into the monitoring data distribution characteristics, power characteristics, fault sensitivity, and validation.

1. Monitoring data distribution characteristics and co-occurrence

To enhance the generalization accuracy of state monitoring and fault warning models, it is crucial to ensure data quality and reveal the distribution patterns of different monitoring data across various fault modes. This targeted approach facilitates the selection of training samples and the establishment of indicator thresholds, thereby reducing both the false omission rate and false alarm rate (FAR) of the models.

To achieve this, we first analyze the failure rates and mean time to repair for each critical component, leveraging historical monitoring data and operational logs to identify the WT components that require close monitoring. Subsequently, after the data preprocessing, we select two representative fault states and analyze the characteristic distributions of various monitoring indexes under different operational conditions. We then extract a dataset related to the operational state from the monitoring database, subsequently identifying diverse distribution patterns and mapping relationships for each monitoring indicator’s data. This involves analyzing the co-occurrence of different monitoring indexes to construct a network matrix that represents the interrelationships among the indexes. Finally, through clustering the matrix, we uncover the patterns and characteristics of various monitoring indexes under different operational health states, providing a foundation for the subsequent study of fault sensitivity.

Among them, the steps of data preprocessing are as follows:

According to the selected dataset and considering the maximum and minimum wind speeds, the interval is divided into N subintervals, where N is a positive integer defined as:

N \approx \frac{V_{\max} - V_{\min}}{0.5}

(1)

Data are then screened where wind speed falls within the interval [ $V_{\min} + (k - 1) \cdot V_{step}, V_{\min} + k \cdot V_{step}$ ] (k = 1, 2, …, N), Here, $V_{step}$ is defined as:

V_{step} = \frac{V_{\max} - V_{\min}}{N}

(2)

Statistical analysis is performed on wind speed data within the kth subinterval: The kth subinterval is further divided into m segments. The expected wind speed value for the kth subinterval is calculated as:

{\bar{V}}_{k} = \sum_{j = 1}^{m} (p_{j} \times v_{j}) (j = 1, 2, \dots, m)

(3)

where $p_{j} = \frac{n_{j}}{n} \times 100 %$ represents the probability distribution of wind speed within the jth segment; $n_{j}$ is the count of wind speed values in the jth segment. This study sets m = 5.

The expected active power value within each subinterval is then calculated using the same method:

{\bar{P}}_{k} = \sum_{j = 1}^{m} (p_{j}^{'} \times p_{j})

(4)

where [ $p_{j}^{'}, p_{j}$ ] denotes the probability distribution of power within the jth subinterval.

Finally, the expected values of wind speed and active power across N subintervals are computed, dividing wind speed and power into N + 1 intervals. Scatter points corresponding to wind-speed-power pairs falling outside these intervals are eliminated, yielding the ideal data samples. This method employs expected values rather than means to characterize SCADA data, reducing statistical errors caused by outliers in SCADA data.

2. Analysis of power characteristics and fault sensitivity

In certain respects, the abnormal temperature rises and excessive vibration levels exhibited by WTs during fault conditions are indicative of energy losses. Hence, an analysis of the power characteristics of WTs and generators under normal and various fault states, in conjunction with the law of energy conservation, serves as a critical basis for understanding the fault sensitivity of monitoring indexes across different fault modes.

To this end, we delve into the impact of two fault states on generator torque, integrating historical fault data samples and the co-occurrence network results of the previously analyzed monitoring indexes. We also compare the extent of output power reduction in WTs due to different faults, particularly examining how the operational health states of WTs influence their output power under varying wind speeds. Subsequently, the distribution characteristics of data samples from several key monitoring indexes under different fault modes have been analyzed, elucidating the influence of power, wind speed, and fault types on the fault sensitivity of monitoring indexes. In addition, we investigate the effects of power and rotational speed on the time-frequency characteristics of vibration signals in the context of gearbox failures, assessing the capacity of various time-frequency features to characterize fault signatures.

Finally, to further validate the accuracy of the aforementioned fault sensitivity analysis results, we incorporate historical SCADA monitoring data samples from two WTs. We develop an adaptive Recurrent Neural Network (RNN) for the key monitoring indexes selected earlier, and through data cleaning, model training, optimization, and testing. AdaRNN was selected for its TDC and temporal distribution matching (TDM) mechanisms, which dynamically segment time-series data into minimally similar sequences. This addresses distribution drift in SCADA data, outperforming static models such as long short-term memory (LSTM) in handling operational variability. The analysis of prediction errors under normal and fault conditions further corroborates and dissects the influence of external stimuli on the fault sensitivity of each monitoring index. The structure of the AdaRNN network is as follows:

This network model consists of two primary modules. The first module employs a TDC approach to accurately quantify the continuous distribution of time-series SCADA data. It identifies temporal similarities in the fluctuation of each monitoring index by dividing the data into k distinct sequences that exhibit the least similarity. The hypothesis is that reducing distribution disparities among these k segments enhances the model’s generalization capability, leading to improved accuracy on previously unseen datasets. A transfer learning model is then applied to these segments, enhancing the model’s robustness to temporal shifts in the data. Finally, the network implements a TDM framework, based on RNNs, to dynamically minimize distribution divergence.⁴⁴

The process of this network involves TDC, pre-training, and TDM. Initially, a time series $D = {D_{1}, \dots, D_{k}}$ composed of k segments is given, where each segment $D_{j} = {x_{i}, y_{i}}_{j = N_{j}}^{N_{j} + 1}$ , and the total length of the time series is $N = \sum_{j = 1}^{k} N_{j}$ , with $N_{0} = 0$ defined. Temporal distribution drift occurs when $P D_{i} \neq P D_{j}$ , $\forall 1 \leq i \neq j \leq k$ , where $P (\cdot)$ denotes the distribution within a sub-time series.

(i) TDC and pre-training

To divide the time series into k distinct fragments while keeping k minimum, time similarity quantization can be expressed as solving the extreme value problem, as follows:

\max_{0 < k \leq k_{0}} \max_{n_{1}, \dots, n_{k}} \frac{1}{k} \sum_{1 \leq i \neq j \leq k} d (D_{i}, D_{j}) s . t . \forall i, Δ_{1} < | D_{i} | < Δ_{2}; \sum_{i} | D_{i} | = n

(5)

where $d (\cdot)$ represents a similarity measurement function, and the maximum mean discrepancies (MMD) is used to measure similarity in this study, $Δ_{1}, Δ_{2}$ , and $k_{0}$ are predefined parameters to avoid meaningless solutions, and a dynamic programming algorithm is then used to solve the above optimization problem.

The objective function in Equation (1) aims to find k optimal time segments that maximize the average distribution differences across periods. This approach ensures two benefits: (1) distinct statistical properties in each time segment and (2) enhanced generalization capability in the resulting predictive model. Correspondingly, we employ the maximum entropy principle to justify this segmentation strategy. Unlike conventional approaches that seek similar periods, the proposed method intentionally identifies dissimilar time segments. The rationale is straightforward: without prior knowledge about time-series segmentation, maximizing distribution diversity across periods naturally maximizes the overall entropy. This creates a more adaptable model for future unseen data. Since test data characteristics are unknown during training, we intentionally train the model under challenging conditions—simulated by maximizing period distribution differences. This worst-case training approach leads to more robust performance in real-world applications.

After k least similar segments are obtained, a method similar to region generalization is designed based on the TDM module to learn the optimal model parameter $θ^{*}$ . The loss of the time distribution matching module used for prediction can be defined as:

L_{pred} (θ) = \frac{1}{k} \sum_{j = 1}^{k} \frac{1}{| D_{i} |} \sum_{i = 1}^{| D_{i} |} ℓ (y_{i}^{j}, M (X_{i}^{j}; θ))

(6)

where $(x_{i}^{j}, y_{i}^{j})$ is the ith sample pair in the sub-time series interval $D_{j}$ , and $ℓ (\cdot, \cdot)$ represents the loss function, with θ representing learnable model parameters.

The training process of each sequence mainly uses the current input $x_{t}$ and the hidden state vector $h_{t - 1}$ of the previous moment to calculate the hidden state vector $h_{t}$ of the current moment, and generates the output $y_{t}$ based on $h_{t}$ . Firstly, the hidden layer state is updated:

h_{t} = σ (W_{hh} h_{t - 1} + W_{hx} x_{t} + b_{h})

(7)

where $W_{hh}$ is the weight matrix between hidden layers, $W_{hx}$ is the weight matrix from the input layer to the hidden layer, $b_{h}$ is the bias term, and $σ (\cdot)$ is the activation function. Then, the output calculation is as follows:

y_{t} = σ (W_{yh} h_{t} + b_{y})

(8)

where $W_{yh}$ represents the weight matrix of the hidden layer to the output layer, and $b_{y}$ is the bias term of the output layer.

Then, the error term and the backpropagation gradient are calculated according to the following formula:

δ_{t} = (y_{t} - t_{t}) \times f^{'} (z_{t})

(9)

where $f^{'} (z_{t})$ is the derivative of the loss function with respect to $z_{t}$ , and the computed gradient is as follows:

\frac{\partial L}{\partial W_{yh}} = δ_{t} h_{t}^{T}

(10)

\frac{\partial L}{\partial b_{y}} = δ_{t}

(11)

\frac{\partial L}{\partial W_{hh}} = \sum_{k = 1}^{t} δ_{k} h_{k - 1}^{T}

(12)

\frac{\partial L}{\partial W_{hx}} = \sum_{k = 1}^{t} δ_{k} x_{k}^{T}

(13)

\frac{\partial L}{\partial b_{h}} = \sum_{k = 1}^{t} δ_{k}

(14)

where $δ_{k}$ represents the error term at moment k. Finally, using gradient descent, the parameters are updated as follows:

W_{yh}' = W_{yh} - l_{r} \frac{\partial L}{\partial W_{yh}}

(15)

b_{y}' = b_{y} - l_{r} \frac{\partial L}{\partial b_{y}}

(16)

W_{hh}' = W_{hh} - l_{r} \frac{\partial L}{\partial W_{hh}}

(17)

W_{hx}' = W_{hx} - l_{r} \frac{\partial L}{\partial W_{hx}}

(18)

b_{h}' = b_{h} - l_{r} \frac{\partial L}{\partial b_{h}}

(19)

where $l_{r}$ represents the learning rate.

(ii) Temporal distribution matching

The TDM component operates on the identified temporal segments to extract transferable patterns by aligning their underlying probability distributions. This approach enables the trained model $M$ to achieve superior generalization performance on novel test datasets when benchmarked against conventional techniques that depend solely on isolated temporal features or aggregate statistical representations. To maintain time dependence during transfer, the time distribution matching module combines a measure of the importance of each time state in a stepped-up RNN cell, expressed as α . This approach allows the importance of each time state to be dynamically learned throughout the training process. For a given time pair ( $D_{i}, D_{j}$ ), the time distribution matching loss is expressed as:

L_{tdm} (D_{i}, D_{j}; θ) = \sum_{t = 1}^{V} α_{i, j}^{t} \cdot d (h_{i}^{t}, h_{j}^{t}; θ)

(20)

where $α_{i, j}^{t}$ is the importance of distribution between $D_{i}$ and $D_{j}$ at state t.

All hidden layer states of this RNN can be calculated according to the standard RNN structure. The rule for evaluating the next hidden state based on the previous hidden state is represented by (·). Its state calculation formula can be defined as:

h_{i}^{t} = δ (X_{i}^{t}, h_{i}^{t - 1})

(21)

The final goal of time distribution matching (single-layer RNN) is denoted as:

L (θ, α) = L_{pred} (θ) + λ \frac{1}{k (k - 1)} \sum_{i, j}^{i \neq j} L_{tdm} (D_{i}, D_{j}; θ, α)

(22)

where represents a hyperparameter used for balancing, the network parameter is pre-trained with all fully labeled data samples to better learn the hidden state and thus facilitate the learning of the parameter α , the pre-trained parameter is represented by $θ_{0}$ .

In combination with $θ_{0}$ , TDM learns the importance of hidden layer states in each RNN cell based on the boosting process. Initially, for each RNN layer, the ownership weight is initialized to the same value within the same layer. Select the cross-domain distribution distance as the enhancement metric. If the distribution distance in (n+1)th learning is greater than the distribution distance in nth learning, then $α_{i, j}^{t, (n + 1)}$ is amplified to enhance its effect of reducing the distribution difference. Otherwise, stay the same. This process can be expressed by the formula:

α_{i, j}^{t, (n + 1)} = {\begin{matrix} α_{i, j}^{t, (n)} \times G (d_{i, j}^{t, (n)}, d_{i, j}^{t, (n - 1)}) & d_{i, j}^{t, (n)} d_{i, j}^{t, (n - 1)} \\ α_{i, j}^{t, (n)} & e l s e \end{matrix}

(23)

where $G (d_{i, j}^{t, (n)}, d_{i, j}^{t, (n - 1)}) = 1 + σ (d_{i, j}^{t, (n)} - d_{i, j}^{t, (n - 1)})$ is the update function for the loss calculation of distribution matching at different learning stages; $d_{i, j}^{t, (n)} = d (h_{i}^{t}, h_{j}^{t}; α_{i, j}^{t, (n)})$ is the distribution distance at the time step t at the nth iteration, and σ(·) is the sigmoid function.

It is easy to see that $G (\cdot, \cdot) 0$ , and then, we can normalize it by the following equation:

α_{i, j}^{t, (n + 1)} = \frac{α_{i, j}^{t, (n)}}{\sum_{j = 1}^{V} α_{i, j}^{t, (n)}}

(24)

By Equations (20) and (23), we can learn the value of α , through multiple iterations, the final values of α are obtained, as well as the regression prediction model for various predictive indexes of WT gearboxes (WTGs).

Through many iterations, the final value of α and the regression prediction model of each prediction index of WT are obtained. Correspondingly, the model was trained using a 70–15–15 train–validation–test split. We employed early stopping (patience = 20 epochs) and Adam optimization. Batch size was fixed at 64 sequences, with input window = 24 h and prediction horizon = 1 h. The other hyperparameters are defined as follows: epochs = 200(max training iterations), learning rate = 0.001(Adam optimizer), sequence length = 24 (input hours).

Data distribution characteristics of each index under various operational status

Failure statistics of WT component

During WT operation, the failure of any major component can lead to shutdowns and significant economic losses. To assess the reliability and average fault clearance times of key WT components, we conducted a comprehensive analysis to identify the components requiring the most attention. Specifically, we analyzed the failure data of critical components from six wind farms, both onshore and offshore, in China over a 5-year period, as illustrated in Figure 2.

Figure 2 reveals that the control system has the highest failure rate among all components of WTs, followed by the electrical system and the mechanical transmission components, particularly the gearbox, which exhibits a relatively high failure rate and the longest average fault clearance time. Thus, real-time monitoring of the operational status of these major components to develop and optimize predictive maintenance strategies is essential to enhance their reliability, extend service life, and reduce the levelized cost of energy.

Data distribution characteristics under various operational status

To further investigate the distributional trends of monitoring indexes under fault conditions across different WTs, we analyzed the key monitoring indexes from both an onshore and an offshore WT. The detailed information of the data used in this article is presented in Table 1. Among these, the selected turbines experienced different types of failures: turbine no. 7, an onshore 2 MW model with a planetary and parallel-stage gearbox, experienced high-speed shaft wear, while offshore turbine no. 19 exhibited a shell crack approximately 1.7 m long and 0.3 m wide at about 14 mfrom the tip of blade no. 1. Figure 3 shows the distribution of key monitoring indexes under normal and abnormal conditions for these turbines. Among these, high-speed shaft wear was confirmed via oil debris spectrometry; blade cracks were verified by drone laser scanning (accuracy: 0.1 mm), documented in maintenance logs.

Table 1.

The detailed information on the data used in this study.

Wind farm type	Turbine no.	Rated power(MW)	Data period	Samplinginterval	Fault type
Onshore	No. 7	2.0	10 April 2018 to 19 December 2019	Once per minute	High-speed shaft wear
Offshore	No. 19	5.0	14 January 2020 to 24 March 2022	Once per minute	Shell crack of blade

Figure 3(a) demonstrates that, during the period of random wind speed fluctuations, the gearbox of turbine no. 7 experienced gradual temperature increases in both the $T_{oil}$ and $T_{HSS - b}$ after the occurrence of high-speed shaft wear at time $t_{1}$ . Between times $t_{2}$ and $t_{3}$ , as the fault condition worsened, these temperatures continued to rise but remained below the system’s alarm and emergency shutdown thresholds. After $t_{3}$ , when the fault was resolved, the temperatures stabilized to normal levels. Figure 3(b) presents the trend of generator torque and active power in offshore turbine no. 19 during the crack in blade no. 1. When the fault was detected during inspection (at time $t_{1}$ ), the turbine was operated under reduced power, with the electromagnetic torque of the generator limited to no more than 11 kN·m, to extend the turbine’s lifespan and provide additional time for spare parts and maintenance logistics.

It can be seen that the operational health of major WT components directly affects whether a turbine can operate normally. The current monitoring system’s fixed alarm thresholds often fail to meet the practical requirements of engineering applications. Hence, it is imperative to develop research into condition monitoring and fault diagnosis using data-driven artificial intelligence algorithms to foster sustainable growth in the wind energy industry.

The co-occurrence analysis among the monitoring indexes under each operational status

Based on the clarified trends in the data distribution characteristics of key indexes under typical failure conditions, we further aim to elucidate the interactions among various monitoring indexes throughout the failure process. To this end, we conducted a co-occurrence correlation study of the monitoring indexes in both normal and fault states. Specifically, we selected 20,000 sets of monitoring data across different power ranges or wind speed ranges for varying health states, filtering for indexes with an average relative abundance of at least 0.05%. We constructed co-occurrence networks under conditions of confidence level p < 0.05and correlation coefficient r > 0.8, as shown in Figures 4 and 5. Table 2 shows the mapping between numbers and monitoring indexes.

Table 2.

Mapping between numbers and monitoring indexes.

No.	Index	No.	Index	No.	Index	No.	Index
ID_1	$T_{HSS - f}$	ID_10	$n_{hub}$	ID_19	$n_{wind 1}$	ID_28	$U_{grid_U}$
ID_2	$T_{HSS - b}$	ID_11	$p_{in}$	ID_20	$n_{wind 2}$	ID_29	$U_{grid_V}$
ID_3	$T_{oil}$	ID_12	$p_{out}$	ID_21	$n_{wind - 10 s}$	ID_30	$U_{grid_W}$
ID_4	$T_{inlet}$	ID_13	$n_{gen}$	ID_22	$n_{wind - 60 s}$	ID_31	$A_{pitch 1}$
ID_5	$T_{cool}$	ID_14	$P_{act - gen}$	ID_23	$n_{wind - 10 m}$	ID_32	$A_{pitch 2}$
ID_6	$P_{act - 60 s}$	ID_15	$T_{gen}$	ID_24	$T_{nac}$	ID_33	$A_{pitch 3}$
ID_7	$P_{act}$	ID_16	$J_{gen}$	ID_25	$n_{hub 1}$	ID_34	$A_{re_pitch 1}$
ID_8	$P_{act - 10 m}$	ID_17	$P_{act - grid}$	ID_26	$n_{hub 2}$	ID_35	$A_{re_pitch 2}$
ID_9	$P_{act - 10 s}$	ID_18	$n_{wind}$	ID_27	$U_{grid}$	ID_36	$A_{re_pitch 3}$

Figure 4 presents the co-occurrence network analysis results for the gearbox of onshore WT no. 7 under normal and fault conditions across various power ranges. The red and green lines represent positive and negative correlations, respectively. From this co-occurrence analysis, it is evident that regardless of health status, the relationship between temperature-related monitoring indexes and both rotor speed and generator speed first diminishes and then increases with power augmentation. In the normal state of the WT, the associations among monitoring indexes become progressively clearer with increasing power and can be broadly categorized into three network modules. In contrast, the network in the fault state is more complex, and the relationships among monitoring indexes can be roughly divided into two modules, with the sign of the correlation between some indexes changing in accordance with health status—for instance, between indicator 3 ( $T_{oil}$ ) and indicator 4 ( $T_{inlet}$ ).

Notably, when the average active power is less than 1000 kW over a 10-min period, the interrelationships among the indexes in the fault state exhibit the highest complexity, with the majority displaying positive correlations. When $1000 kW \leq P_{act - 10 m} 2000 kW$ , nearly all correlations among the indexes in the fault status are positive. However, once rated power is reached, many negative correlations among monitoring indexes emerge. This suggests that the richness of positive correlations among indexes in the fault status increases with power, followed by a subsequent decrease.

Figure 5 illustrates the co-occurrence analysis results for offshore WT no. 19, comparing conditions of blade crack failure with normal operation. It is observable that as wind speed increases, the complexity of correlations among monitoring indexes initially decreases and then increases. Importantly, the sign of the correlations among indexes within the same power range varies significantly between different operational statuses, and these relationships are influenced by external stimuli such as wind speed and power. These phenomena collectively indicate that the changes in correlations among monitoring indexes vary significantly across different failure modes, presenting substantial challenges in accurately identifying and extracting fault characteristics from multidimensional sensor information, particularly under lower power conditions.

Fault sensitivity analysis and verification of each monitoring index

Based on clarifying the distribution characteristics of monitoring indexes for WTs under varying operational status and power levels, this study further investigates the power characteristics of WTs and the fault sensitivity of various monitoring indexes under different fault types. This research provides essential boundary conditions and foundations for the condition monitoring and fault diagnosis of critical components in WTs.

Output power characteristics of the WTs

In general, when key components of WTs—especially those involved in energy capture and transmission—sustain faults, their energy transfer efficiency diminishes. Therefore, investigating the output characteristics of WTs and generators under various fault conditions and wind speed ranges is essential for understanding fault sensitivity.

Power characteristics of generators

The mechanical efficiency of WTs suffers due to faults, which inevitably influence generator torque under consistent control commands, thereby being impacted by the health status of the WTG. Correlation analysis reveals that, under normal operating conditions, the average wind speed over a 10-s interval demonstrates the strongest correlation with generator torque and speed.⁴³ Therefore, this section investigates the torque-speed mapping relationship of the generator at varying wind speeds, based on the average wind speed over 10 s, to indirectly reflect the output characteristics of WTs under different health states. This research presents a statistical analysis of the output characteristics of the generators in onshore WT 7 and offshore WT 19, considering both normal and abnormal conditions. The statistical results for generator speed and torque across different wind speed ranges are illustrated in Figures 6 and 7.

In Figure 6, $l_{n - J}$ denotes the control curve for generator speed and torque. Prior to achieving the rated wind speed, scattered points ( $A_{Down}$ ) in the fault condition exhibit deviations from the curve $l_{n - J}$ . Conversely, during normal operation, the absence of nonlinear control effects results in a relatively straightforward correlation between speed and torque, with nearly all points clustering around the curve. When wind speeds exceed the rated threshold, some data points in the fault condition do diverge from the curve $l_{n - J}$ ; however, the distribution exhibiting fault characteristics remains largely indistinguishable from that under normal conditions prior to reaching the rated wind speed.

Figure 7 depicts the output characteristics of the generator in offshore WT 19 under both normal and abnormal conditions. Before reaching the rated wind speed, there are relatively few scattered points ( $A_{Down}$ ) deviating from the curve $l_{n - J}$ . As wind speeds approach or reach the rated level, frequent adjustments of the pitch system and electromagnetic torque to reduce operational power—intended to prolong the lifespan of the WT—complicate the correlation among various monitoring indexes. Consequently, certain data points in the fault condition exhibit notable deviations from the curve $l_{n - J}$ .

Above this, to fully exploit the capacity of monitoring indexes for detecting potential faults in key components of WTs under varying wind speed (power) conditions, it is imperative to meticulously cleanse and classify the training dataset according to the established correspondence between wind speed and power. Beyond the exclusion of monitoring data from shutdown or low-power states and the rectification of anomalies from other causes, careful consideration must be given to the allocation of weight coefficients between health assessment indexes of the WTG and the feature dimensions of predictive models under different power states. This strategy aspires to develop a more accurate predictive model for indexes, thereby minimizing the rates of false negatives and false positives in fault detection.

Power characteristics of WT

The quality of SCADA monitoring data is simultaneously affected by factors such as wind shear, turbulence, and the operational status of WTs. In this study, we analyze historical SCADA monitoring data from onshore WT no. 7 and offshore WT no. 19 under both normal and abnormal conditions. This analysis integrates the interrelationships among various monitoring indexes to explore the mutual influences of wind speed, power output, and temperature, to clarify how power characteristics impact the fault sensitivity of WTs.

Figure 8 presents the wind speed-power fitting curves, standard wind speed-power curves, and power discrepancies for the selected WTs under varying operational states. The fitting curves for the fault and normal conditions are depicted by orange and green lines, respectively, while the black line represents the standard wind speed-power curve, and the blue line illustrates the power discrepancies in both normal and fault states.

As shown in Figure 8, when wind speeds fall below the rated speed, the actual output power is consistently below the standard wind speed-power curve. Upon reaching the rated wind speed, actual output power is typically increased by approximately 10% to enhance energy production. The figure indicates that power discrepancies initially increase with wind speed, followed by a subsequent decrease. Notably, for the onshore turbine no. 7 experiencing gearbox failure, a significant power discrepancy is evident in the wind speed range of approximately,^10,13 peaking at 78.06 kW. In contrast, offshore turbine no. 19, affected by blade damage, exhibits a substantial power discrepancy, reaching a maximum of 667.16 kW within the wind speed range of [4.5, 9].

According to the law of conservation of energy, power discrepancies are a critical contributor to abnormal temperature elevations and vibrations exceeding normal thresholds. This implies that the significance of fault characteristics in WTs is positively correlated with power discrepancies to some extent. The findings of this research provide valuable insights for data cleansing and establishing weight coefficients for various monitoring data in the field of fault diagnosis for WT condition monitoring.

Fault sensitivity of each monitoring index

The fault sensitivity of the monitoring indicator refers to its inherent capability to reliably and detectably change its characteristic pattern or value in direct response to the emergence or progression of a specific fault within the WT system. A highly sensitive indicator exhibits significant, consistent, and early deviations from its normal operational baseline when a particular fault occurs, even at incipient stages, while remaining relatively stable under normal operating conditions and minor, non-fault-related disturbances. Crucially, the fault sensitivity encompasses not only the magnitude of the change but also its specificity and its timeliness. In practical WT condition monitoring and diagnostics, identifying and utilizing indicators with high fault sensitivity is paramount, as they form the foundation for effective early fault detection, accurate fault isolation, and precise severity assessment, ultimately enabling proactive maintenance and minimizing downtime.

During the operation of WTs, the sensitivity of various monitoring indexes differs depending on the location and type of fault. Thus, it is necessary to combine fault logs with the prominence of fault characteristics and data quality of each monitoring index under different operational statuses to identify those indexes that exhibit significant trends or fault signatures during fault status. In this study, we take the seventh onshore WTG as a case study, utilizing historical SCADA and CMS monitoring data under both normal and abnormal conditions to further analyze the distribution characteristics of different monitoring indexes during fault status. This section presents a sensitivity analysis of monitoring indexes to faults. Sensitivity is quantified via: (i) deviation magnitude of indexes between normal/fault states (Figures 9 and 10), (ii) changes in time-frequency features (Figure 11), and (iii) prediction error shifts (Figure 12). Results confirm that sensitivity is highly dependent on operational conditions.

Temperature monitoring index of each monitoring index

From the co-occurrence analysis in “The co-occurrence analysis among the monitoring indexes under each operational status” section and previous research, it is evident that most temperature-related monitoring indexes are highly correlated with the 10-min average active power. Therefore, this indicator is used as the independent variable for each temperature indicator. By consulting the fault logs, 30,000 data samples are randomly selected within an ambient temperature range of −10 to 10°C from monitoring data that excludes all downtime. These samples are then uniformly divided into intervals, and the mean value within each interval is calculated, as shown in Figure 9.

As illustrated in the figure, the temperature of the aforementioned monitoring indexes shows significant differences between normal and abnormal status. The magnitude of this difference is positively correlated with active power; that is, the higher the power, the more pronounced the fault characteristics. In the fault state, as seen in Figure 9(a) and (b), due to power constraints, only a small fraction of temperature readings reach or exceed the fast shutdown threshold of 80°C, leading to underreporting of anomalies and delayed maintenance, which results in considerable economic losses. Figure 9(c) demonstrates that in the normal state, temperatures remain below the thresholds for activating the water pump and fans, indicating that the cooling system is highly effective in maintaining the oil sump temperature of the gearbox. However, under fault conditions, a substantial portion of oil temperatures exceeds the fan activation threshold but remains far below the system alarm threshold of 75°C and the fast shutdown threshold of 80°C. Figure 9(d) shows that under normal conditions, inlet oil temperature is negatively correlated with power, whereas it exhibits a positive correlation under fault conditions.

Vibration signal of the WTG

To investigate the influence of power levels, rotational speed, and operational status on the time-frequency characteristics of CMS vibration signals, we selected five sets of CMS vibration data for analysis, focusing on similar power levels and rotational speeds. These datasets were collected from the seventh onshore WTG under both normal conditions and abnormal conditions induced by high-speed shaft wear as documented in the fault logs. The specific rotational speeds, power levels, and high-speed shaft rotational frequencies are listed in Table 3 (sampling frequency: 25,600 Hz; sampling points: vertical radial direction of the high-speed shaft; sampling duration: 1 s).

Table 3.

Distribution of rotational speed and active power under each operational status.

Categories	Normal			Abnormal
Categories	Rotational speed (r/min)	$f_{HSS \times 1}$ (Hz)	$P_{act}$ (kW)	Rotational speed (r/min)	$f_{HSS \times 1}$ (Hz)	$P_{act}$ (kW)
1	1108.4	18.47	119	1108.3	18.47	36
2	1602.5	26.71	374	1602.5	26.71	376
3	1736.7	28.94	417	1736.6	28.94	390
4	1757.8	29.30	1024	1757.8	29.30	1002
5	1764.9	29.42	309	1764.9	29.42	263

To accurately extract fault information from the vibration signals, both frequency-domain and time-domain features were derived from each data set. The time-frequency characteristics of the selected five CMS vibration signal sets are shown in Figure 10.

In Figure 10, blue traces denote signals under normal conditions, red traces denote signals under abnormal conditions (high-speed shaft wear), and time-domain waveforms (left subplots) show signal amplitude (m/s²) versus time (s). Frequency-domain (right subplots) display amplitude versus frequency (Hz), highlighting key components such as shaft rotational frequencies and sidebands. Thereinto, sidebands refer to symmetrical frequency components that appear around a dominant frequency (such as shaft rotational frequency or gear meshing frequency) in the spectrum, resulting from amplitude or frequency modulation caused by periodic mechanical disturbances. These disturbances typically stem from localized defects (e.g., bearing spalls, gear tooth damage, or shaft imbalances) that induce cyclic variations in vibration amplitude or phase. The spacing between sidebands corresponds to the modulating frequency (e.g., shaft rotation rate or bearing fault frequency), while their amplitude and prominence directly indicate the severity and progression of the underlying fault, making them critical diagnostic markers for identifying incipient mechanical issues before catastrophic failure.

In the first set, when the rotational speed and power are relatively low, the vibration amplitude caused by high-speed shaft wear is significantly higher compared to normal conditions, and the amplitude of the first-order fault frequency of the high-speed shaft in the frequency domain increases. In the second set, at a rotational speed of 1602.5 r/min, the vibration signal amplitude increases under fault conditions, with more pronounced sideband components appearing at the first-order rotational frequency of the high-speed shaft. In the third and fourth sets, where the rotational speeds are 1736.7 and 1757.8 r/min and the power levels are 417 and 1024 kW, respectively, the maximum amplitude in normal conditions increases with power, with the first-order rotational frequency amplitude of the high-speed shaft fluctuating around $0.04 m / s^{2}$ . Under fault conditions, the sideband characteristics intensify with increasing power, while the first-order rotational frequency amplitude decreases. In the fifth set, both rotational speed and power are similar to those in the third set, and the amplitude and spectral characteristics exhibit similar trends across different health states.

The root mean square (RMS) amplitude demonstrates a positive correlation with both rotational speed and power output, while exhibiting significant elevation under fault conditions—as exemplified by group 1 where RMS values increase by over 100% in the abnormal state. At higher operational speeds, kurtosis and skewness metrics converge between normal and faulty states, underscoring the critical importance of time-frequency analysis for detecting incipient faults during low-speed operation. Consequently, the integrated use of time-domain features (RMS, kurtosis) and frequency-domain characteristics (peak amplitudes, sidebands) enables unambiguous identification of high-speed shaft wear, with time-frequency analysis proving particularly effective for quantifying fault-induced amplitude modulations (sidebands), detecting early-stage faults at low speeds/power (e.g., group 1), and decoupling load effects (power variations) from genuine fault signatures (as demonstrated in groups 3–5).

Verification of fault sensitivity

To further validate the scientific rigor behind the selection of indexes and prediction dimensions following correlation analysis—alongside an assessment of fault sensitivity, data distribution characteristics, and the power dynamics of WTs under varying operational status—this section integrates historical monitoring data from onshore WT 7 and offshore WT 19 across different fault modes. An adaptive deep learning algorithm is employed to develop a network model that performs regression predictions on key monitoring indexes in both normal and abnormal operational conditions. The selected key monitoring indexes for WT 7 are 1, 2, 3, and 4, and the same indexes are designated for WT 19. The resulting percentage errors of the regression predictions are illustrated in Figure 12.

From Figure 12, it is evident that, under high-speed shaft wear faults in WT 7, the regression prediction percentage errors for the four selected key monitoring indexes exhibit markedly different trends between normal and abnormal status. Notably, the prediction errors for inlet oil temperature and oil temperature are significantly larger in the fault state. In contrast, the prediction error for the high-speed side rear-end temperature lacks sufficient distinction between normal and abnormal operational conditions. Similarly, for WT 19, the prediction percentage errors during blade crack faults and normal states also display significant differences. This underscores that the selected key monitoring indexes, grounded in fault sensitivity analysis, effectively characterize the operational status of WTs.

To further investigate the mapping relationship between the prediction errors of each key monitoring indicator in normal and fault states and wind speed, we categorize wind speed into eight intervals and compute the average prediction percentage errors within these intervals under different operational statuses, resulting in radar charts as depicted in Figures 13 and 14.

Figure 13 illustrates that the average prediction errors of the four selected key monitoring indexes reveal pronounced differences between normal and abnormal status. In particular, the prediction errors for $T_{HSS - f}$ and $T_{oil}$ increase with rising wind speeds in the abnormal status, indicating that fault characteristics become more pronounced at higher wind speeds. For the $T_{HSS - b}$ , the prediction errors exhibit a trend of first increasing and then decreasing as wind speed rises, mirroring the changes observed in the power differentials of the WT. Furthermore, the prediction error for $T_{inlet}$ indicates that the difference between normal and fault states is minimally affected by wind speed, establishing this index as a crucial metric for assessing the operational status of the WTG.

Figure 14 presents the prediction results for WT 19. It is clear that the average prediction percentage errors for the four selected key monitoring indexes in the normal status are minimally influenced by wind speed, whereas in the abnormal status, average prediction errors increase with rising wind speeds. This phenomenon can be attributed to the fact that the blades are critical components for harnessing wind energy, and their operational status directly affects the efficiency of energy capture. Thus, closely monitoring the relationship between the power output of the WT and wind speed is essential for evaluating the operational status of the blades.

Through comparative evaluation of time-series prediction methods, we assess each parameter’s regression accuracy, FAR, and missed fault rate (MFR) to determine the optimal prediction approach for every monitored metric. We calculate the mean (μ) and standard deviation (σ) of the prediction percentage errors (PEs) under normal operational conditions for the 7th and 19th WTs. We define a fault detection threshold ( $l_{th} = μ + x \cdot σ$ ), where x is a predetermined positive integer aimed at selecting an optimal value to minimize the combined sum of FAR and MFR for each monitoring indexes. The specific analysis results are shown in Table 4.

Table 4.

Fault detection performance (FAR and MFR) of AdaRNN for different monitoring indices.

Number and categories		FAR (%)	MFR (%)
No. 7	$T_{_HSS - f}$	6.40	4.20
	$T_{_HSS - b}$	5.40	4.40
	$T_{_oil}$	0.80	1.60
	$T_{_inlet}$	5.00	6.40
	Mean	4.40	4.51
No. 19	$P_{act}$	1.60	0.95
	$P_{act - 10 s}$	3.20	3.40
	$P_{act - 60 s}$	2.20	1.30
	$P_{act - 10 \min}$	1.60	1.20
	Mean	2.15	1.71

FAR: false alarm rate; MFR: missed fault rate.

Based on the performance metrics in Table 4, the analysis reveals distinct patterns across monitoring indices: For temperature parameters (group no. 7), $T_{_oil}$ achieves the lowest FAR = 0.80% and MFR =1.60%, outperforming other temperature indices like $T_{_HSS - f}$ (FAR = 6.40%, MFR = 4.20%) and $T_{_inlet}$ (MFR = 6.40%), with group means of 4.40% FAR and 4.51% MFR. For active power parameters (group no. 19), real-time $P_{act}$ demonstrates the lowest MFR (0.95%), while longer forecasting horizons, $P_{act - 10 \min}$ yield lower errors (FAR = 1.60%, MFR = 1.20%) compared to short-term $P_{act - 10 s}$ (FAR = 3.20%, MFR = 3.40%), resulting in superior group means (FAR = 2.15, MFR = 1.71%). Critically, power metrics exhibit consistently better fault detection than temperature metrics, with $T_{_oil}$ emerging as the most reliable temperature-based indicator—aligning with the reported mean gearbox oil temperature prediction performance (FAR = 3.275%, MFR = 1.2%).

To quantify prediction uncertainty, we computed 95% confidence intervals (CIs) for the prediction PEs using the mean (μ) and standard deviation (σ) of PE distributions under normal and fault conditions (Table 4, Figure 12). The CI is defined as: CI = μ ± 1.96σ. For WT 7 (gearbox faults), we used the PE statistics of $T_{_oil}$ (μ = 2.1%, σ = 0.9% under normal conditions; μ = 13.7%, σ = 1.8% under abnormal conditions). For WT 19 (blade cracks), we used $P_{act - 10 \min}$ (μ = 1.2%, σ = 0.4% under normal conditions; μ = 4.5%, σ = 1.2% under abnormal conditions). $T_{_oil}$ under normal operation exhibits CI = [0.336%, 3.864%], while under high-speed shaft wear, CI = [10.172%, 17.228%]. This wider CI during faults reflects increased model uncertainty due to anomalous data distributions. Similarly, $P_{act - 10 \min}$ for WT 19 shows CI = [0.416%, 1.984%] (normal) versus [2.148%, 6.852%] (abnormal), indicating degraded prediction stability during blade cracks.

These results confirm that AdaRNN enhances early warning robustness, particularly for gearbox oil temperature and longer-horizon power forecasts.

Discussion

As illustrated in Figures 6 and 7, the power characteristics of the generator under different fault modes reveal that when wind speeds approach or reach rated levels, the frequent intervention of the pitch control system complicates the nonlinear relationships among various monitoring indexes. Therefore, during condition monitoring, it is imperative to consider the weight distribution coefficients of the monitoring indexes across different fault modes; neglecting this can easily lead to the missed detection of potential faults.

Figure 8 presents the power characteristics of the entire system under varying fault modes, indicating that prior to the WT achieving rated power, the fault characteristics (the power differential between pre-fault and fault conditions) become increasingly pronounced with rising wind speeds. Consequently, to obtain accurate predictions of operational status, it is essential to utilize SCADA monitoring data collected during higher power states for training predictive models, thereby allowing for the assessment of the operational condition of the WTG based on prediction errors. Moreover, analysis of the “wind speed-power” curve for the gearbox in a normal status, both before fault occurrence and after fault resolution, suggests that the predictive model derived from pre-fault evaluation metrics may not adequately meet the requirements for post-fault monitoring. Hence, retraining the predictive model using monitoring data obtained after fault resolution is necessary to achieve the desired generalization accuracy in condition monitoring.

The fault sensitivity analysis of SCADA and CMS data across different fault modes indicates that under high-speed shaft wear faults in the gearbox, various temperature indexes struggle to reach their designated alarm thresholds. Consequently, relying solely on the real-time values of these monitoring indexes to assess the operational status of critical WT components would likely render the fault detection insufficient for practical engineering requirements. The practical utility of the proposed method for detecting potential WTG faults is validated by correlating time-frequency analysis outcomes from CMS data with corresponding historical fault logs. While current research into regression prediction for SCADA monitoring indices in WTGs, utilizing machine learning techniques including Support Vector Machines, Bayesian Networks, Back Propagation Neural Networks, and LSTM networks, demonstrates that LSTM achieves higher generalization accuracy by effectively modeling the sequential nature of the data,⁴⁵ the approach presented herein demonstrates distinct advantages for practical fault detection. Unlike the focus of the study by Wang et al.⁴⁵ on predictive accuracy within regression tasks, the current methodology directly targets and verifies the identification of incipient faults through empirical CMS-fault log alignment, offering complementary strengths specifically tailored for real-world diagnostic practicability.

Furthermore, regression prediction error analyses of the key monitoring indexes, considering non-operational status, reveal that in most scenarios characterized by lower wind speeds, the distinction between the prediction errors of normal and abnormal status is minimal. However, as wind speed increases, the fault characteristics become increasingly evident. Therefore, it is essential to develop and train separate predictive models for each monitoring indicator based on different wind speed or power ranges to enhance the accuracy of fault detection.

Conclusion

This study establishes data-driven guidelines for optimizing condition monitoring and predictive maintenance of WTs, leveraging distribution characteristics and fault sensitivity analyses of SCADA/CMS data from onshore and offshore installations. By validating AdaRNN against historical operational datasets, we identify critical relationships between fault signatures, environmental stimuli, and power output dynamics. Our findings provide actionable strategies for enhancing early fault detection and reducing false alarms in wind farm operations. The study yields the following conclusions:

Component failure statistics reveal that control systems, electrical systems, and gearboxes exhibit the highest failure rates and downtime, necessitating prioritized real-time monitoring. Crucially, distribution characteristics of temperature and vibration indexes shift significantly under faults, yet fixed alarm thresholds often miss early anomalies due to power-dependent fault sensitivity. To address this, we recommend implementing dynamic threshold adjustments based on real-time power output—lowering sensitivity during sub-1000 kW operation and increasing vigilance near rated power.

Fault sensitivity analyses further demonstrate that vibration metrics (RMS, kurtosis) and generator torque deviations offer the most reliable fault indicators, but their effectiveness varies with operational states. For gearboxes, RMS vibration sensitivity increases by 40–60% at >1500 kW loads, while blade cracks cause power discrepancies >600 kW at low-wind speeds (4.5–9 m/s). Thus, component-specific monitoring protocols should be deployed: vibration-based CMS for gearboxes at high loads, and power-curve deviation tracking for blades across all wind speeds.

Validation via AdaRNN confirms that inlet oil temperature and gearbox oil temperature exhibit the strongest fault sensitivity, particularly at wind speeds >8 m/s. However, low-wind conditions (<6 m/s) obscure these signatures. We therefore advocate training condition-based maintenance models on power-stratified data: high-wind (>8 m/s) segments for temperature indexes, and medium-to-high power (>1000 kW) segments for vibration features, to maximize detection accuracy.

In summary, the conclusions drawn from this study are grounded in actual engineering data and thus can serve as a technical reference for the development of predictive maintenance strategies in wind farms. Future research should aim to integrate monitoring data, model simulation results, and experimental data using advanced algorithms such as digital twins and transfer learning. This integration is expected to enhance the generalization and accuracy of operational condition prediction models for WTs, especially those deployed in offshore environments. The ultimate objective remains the formulation and optimization of predictive maintenance strategies that improve the operational reliability of wind turbines.

Footnotes

Appendix

Notation

$T_{HSS - f}$	temperature of the front in the high-speed shaft (°C)	$n_{wind 1}$	instantaneous wind speed (sensor 1) (m/s)
$T_{HSS - b}$	temperature of the back in the high-speed shaft (°C)	$n_{wind 2}$	instantaneous wind speed (sensor 2) (m/s)
$T_{oil}$	temperature of gearbox oil (°C)	$n_{wind}$	instantaneous wind speed (m/s)
$T_{inlet}$	temperature of inlet lubricant (°C)	$U_{grid}$	average voltage of network (V)
$T_{cool}$	temperature of cooling water (°C)	$U_{grid_U}$	U-phase voltage of the grid (V)
$n_{hub 1}$	rotate speed of the hub (sensor 1) (r/min)	$U_{grid_V}$	V-phase voltage of the grid (V)
$n_{hub 2}$	rotate speed of the hub (sensor 2) (r/min)	$U_{grid_W}$	W-phase voltage of the grid (V)
$n_{hub}$	rotate speed of the hub (r/min)	$A_{pitch 1}$	variable pitch angle of no. 1 blade (°)
$n_{gen}$	generator rotate speed (r/min)	$A_{pitch 2}$	variable pitch angle of no. 2 blade (°)
$P_{act - 10 m}$	average active power in 10 min (kW)	$A_{pitch 3}$	variable pitch angle of no. 3 blade (°)
$n_{wind - 10 m}$	average wind speed in 10 min (m/s)	$A_{re_pitch 1}$	redundant pitch angle of no. 1 blade (°)
$P_{act - 60 s}$	average active power in 60 se (kW)	$A_{re_pitch 2}$	redundant pitch angle of no. 2 blade (°)
$n_{wind - 60 s}$	average wind speed in 60 s (m/s)	$A_{re_pitch 3}$	redundant pitch angle of no. 3 blade (°)
$P_{act}$	active power of the wind turbine gearbox (kW)	RNN	Recurrent Neural Network
$P_{act - gen}$	active power of the grid (kW)	MMD	maximum mean discrepancy
$P_{act - grid}$	active power of the grid (kW)	O&M	operation and maintenance
$n_{wind - 10 s}$	average wind speed in 10 s (m/s)	WTG	wind turbine gearbox
$T_{nac}$	temperature of nacelle (°C)	SCADA	supervisory control and data acquisition
$p_{in}$	pressure of the inlet lubricant (bar)	CMS	condition monitoring system
$P_{act - 10 s}$	average active power in 10 s (kW)	AdaRNN	adaptive recurrent neural network
$T_{gen}$	maximum temperature of generator windings (°C)	RMS	root mean square
$p_{out}$	pressure of the outlet lubricant (bar)	TDC	temporal distribution characterization
$J_{gen}$	torque of the generator (kN·m)	TDM	temporal distribution matching

Author contributions

Yongchao Zhu: data curation, formal analysis, methodology, software, writing—original draft, funding acquisition. Caichao Zhu: conceptualization, supervision. Changming Zhang: formal analysis, writing—review and editing. Jianjun Tan: formal analysis, validation, and funding acquisition. Haifeng He: writing—review and editing. Lei Rao: data curation, investigation.

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully appreciate the support from the National Key Research and Development Program of China (no. 2023YFB4203100), Scientific Research Plan Projects of Shaanxi Education Department (no. 24JR043), Shaanxi Science and Technology Department Project (no. 2025JC-YBQN-618), and Doctoral Program of Shaanxi University of Technology (SLGRC202402).

ORCID iD

Yongchao Zhu

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