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Coupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics‐data‐fusion‐based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time‐varying fault characteristics were determined using real‐time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real‐time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES. [ABSTRACT FROM AUTHOR]

High reliability and security have become the hallmarks of complex electromechanical systems. Owing to the difficulties in fault data collection, ambiguity and uncertainty have been inevitably associated with complex electromechanical systems. Thus, the ability to perform reliability evaluation using scarce fault data is of immense significance to these machines and is the focus of this study. A similarity based cloud model is proposed to evaluate the running state of complex electromechanical systems. By combining objective and subjective factors, the reliability of complex electromechanical systems is evaluated by calculating the similarity between the cloud models of actual and standardised states. Next, the inverter of an offshore wind turbine is used to verify the accuracy and effectiveness of the proposed approach. The cloud model based framework for reliability evaluation inherits the preponderance of the uncertainty problem, overcomes the drawbacks of the current reliability approaches, and provides a theoretical basis, as well as a practical approach for the maintenance and repair of complex electromechanical systems with missing fault data. Additionally, it also provides a new methodology for solving the uncertainty problems caused by paucity of data. [ABSTRACT FROM AUTHOR]

• A physics-guided, data-refined fault root cause tracing framework is proposed. • A physics-guided initial hierarchical network model is established. • Operation data-refined model updating algorithm is designed. • A novel bidirectional reasoning-based fault node ranking strategy is proposed. • A fault root cause tracing application of an offshore wind turbine is conducted. Fault root cause tracing (FRCT) is critical for the safety assurance of complex electromechanical systems. However, it is still a challenging task due to the complexity, uncertainty and time-varying characteristics of limited known fault development and propagation mechanism. Therefore, this paper proposed a physics-guided, data-refined FRCT framework. First, a physics-guided hierarchical fault root cause tracing network (HFTN) model is defined and constructed based on the statistics fault mechanism while considering fault development and propagation characteristics including network, hierarchy, and uncertainty. Second, an operation data-refined algorithm is designed to update the initial model, where Wasserstein Generative Adversarial Network and Long Short-Term Memory-based local anomalies detection, and statistical failure laws-based global dynamic fault mechanism reflection are introduced. Third, a novel bidirectional probabilistic reasoning strategy is developed to rank the real-time probabilities of fault causes in HFTN, which combines both faults reverse diagnostic and forward predictive knowledge to improve the results stability. The research is evaluated by an offshore wind turbine FRCT application, the research-assisted dynamic reliability analysis and identification of compound fault are also explored for potential application. This research combines common and individual properties of faults, has excellent accuracy and interpretability, and is expected to support integrated research of system safety. [ABSTRACT FROM AUTHOR]

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts' diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members' diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis. [ABSTRACT FROM AUTHOR]