A new memory-coupled multi-regime NHPP framework for reliability growth with partial reset

Reliability growth modeling plays an important role in the analysis of repairable systems operating under evolving and non-stationary conditions; however, classical formulations such as Crow–AMSAA and Musa–Okumoto generally rely on single-regime assumptions that may limit their ability to represent heterogeneous failure dynamics and imperfect maintenance effects across successive operational phases. This paper proposes a Multi-Rate Reliability Growth Model with Partial Reset (MR-RGM-PR), a multi-regime NHPP-based probabilistic framework integrating regime-dependent dynamics and intensity-level memory propagation mechanisms to represent residual effects induced by imperfect corrective actions. An estimation framework combining maximum likelihood estimation and Bayesian inference is developed to support parameter identification and uncertainty quantification, while the theoretical analysis demonstrates that the proposed formulation preserves the principal properties required for cumulative counting processes, including continuity, monotonicity, and non-negativity, while enabling flexible representation of regime transitions and inter-regime dependency propagation. Validation experiments conducted on both synthetic and real-world datasets suggest that the proposed framework provides improved goodness-of-fit characteristics and reduced prediction discrepancies relative to several classical reliability growth formulations under the considered experimental conditions, as reflected by lower RMSE and MAE values together with improved information criteria. The results additionally highlight the potential relevance of combining adaptive segmentation and memory propagation mechanisms for representing complex reliability dynamics involving heterogeneous operational conditions and partially effective maintenance actions. From a practical perspective, the proposed MR-RGM-PR framework may support the identification of critical operational phases and provide useful insight for maintenance planning, intervention-policy optimization, and reliability-oriented decision support in industrial environments.