Dirichlet probability navigated fault detection via key-group memory auto-encoder under non-stationary working conditions

Abstract

Real-time condition monitoring is the foundation of prognostics and health management (PHM) for mechanical systems. Constraint by extravagant cost and insurmountable obstacles to simulate real faults in actual working conditions, establishing fault detection models with historical normal data is the most promising way. Meanwhile, non-stationary fault detection is rarely studied in the literature but fits most in real working conditions. Thus, an innovative method named KGMem-DirAE is proposed to conduct real-time fault detection under non-stationary working conditions using normal data only. In the training stage, normal patterns with different semantics are recorded in the developed key-group memory module (KGMem) combined with auto-encoder. Considering the matching probabilities of encoded latent features and recorded memory units (normal patterns), a statistical anomaly score defined by aggregated negative log-likelihood of Dirichlet distributions is proposed to detect incipient failures. In order to deal with time-varying working conditions and capture fault degradation trends concurrently, normalized time-frequency maps containing fault-evolving information are obtained from vibration data. Both widely studied non-stationary and stationary run-to-failure data sets are deeply researched using the proposed KGMem-DirAE and the experimental results proved its superiority against other comparative anomaly detection methods.