GCVIF: Pioneering Explainable Domain-Shared Representation Learning for Fault Signal Detection in Multiple Working States Simultaneously

With the rapid development of sensor systems brought about by the industrial Internet of Things process, the need to unsupervisedly detect fault signals under multiple working conditions simultaneously has led to the emergence of multitarget domain adaptation (MTDA). This advancement is confronted by two primary challenges on domain adaptation: 1) fault feature extraction prefers target domains akin to the source domain, often sidelining others and 2) the learned features’ lack of interpretability. To address these, this article proposes the generalized-Gaussian cyclic variational inference framework (GCVIF). This framework engages the generalized-Gaussian cyclostationary distribution to initially capture the non-Gaussian and nonstationary attributes of fault signals, with an extended likelihood ratio test proposed to estimate distribution parameters. Leveraging these estimated distributions as priors, the generalized-Gaussian cyclic variational autoencoder is then developed to infer domain-shared representations. The process is steered by a specialized domain-shared representation learning principle, focusing on compact representation in the encoder and cyclostationary structure reconstruction in the decoder. Remarkably, extensive fault detection trials affirm that leveraging distributions estimated unsupervised as priors enables unbiased feature extraction, and the inference of domain-shared representations is inherently aligned with fault cyclostationary pulses simulation on the signal time domain, ensuring their direct mechanistic explainability.