Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system

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.