Bidirectional Sensitive Feature Generation Network for Generalized Zero-Shot Fault Diagnosis Considering Single and Compound Faults

Generalized zero-shot fault diagnosis (GZS-FD) is a challenging and commonly encountered issue due to the coexistence of seen and unseen fault types. To deal with single and compound unseen faults and generate deceptive synthetic unseen fault samples, the bidirectional sensitive feature generation network (BSFGN) for GZS-FD framework is proposed, which operates through three sequential modules. First, the multidomain sensitive feature selection module selects the informative features through the dependencies of features from multidomain, which are then input to the BSFGN module to generate deceptive synthetic features for diverse unseen faults. Finally, the GZS-FD module leverages both synthetic unseen fault samples and real seen fault samples for fault diagnosis. Experiments on the designed feed drive system testbed validate the effectiveness of the framework, demonstrating superior diagnostic accuracy over state-of-the-art methods when considering single and compound faults.