Knowledge Transfer and Reinforcement Based on Biunbiased Neural Network: A Novel Solution for Open-Set Fault Transfer Diagnosis

Abstract

Fault transfer diagnosis is a key technology to ensure the reliability and safety of industrial systems, the core of which is to identify the health status of the equipment among different working conditions with multiclassification methods. However, most of them are based on a closed-set assumption that the label space among different working conditions is consistent, which is hard to satisfy in a practical industrial environment as unknown faults would inevitably occur during operation, i.e., the open-set fault transfer diagnosis (OSFTD) problem. Moreover, during the transfer process, unnecessary source-specific knowledge tends to be adapted, which brings about biased diagnostics on both domain and category. Aiming at this issue, an OSFTD framework, coined as knowledge transfer and reinforcement based on biunbiased neural network (KTR-BUNN), is proposed. First, a domain-unbiased knowledge transfer subnet is proposed, including an uncertainty-aware fault transferability evaluator (FTE) that estimates the transferability of target-domain samples unbiasedly to guide distribution alignment of known faults and a triple-tier unknown fault separator (UFS) that takes transferability as the criterion to extrapolate unknown faults. Second, a class-unbiased knowledge reinforcement subnet is designed to promote the recognition of fault semantic features at the embedding space, where fault knowledge graphs (FKGs) are constructed to describe the relationships between fault types, and they are optimized by a contrastive fault correlation loss, so that fine-grained class-level fault features can be further aligned. The knowledge transfer and knowledge reinforcement mechanisms work jointly to facilitate the performance of OSFTD. Finally, extensive experimental results conducted on diverse diagnostic tasks illustrate the superiority of the proposed KTR-BUNN.