A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes

Abstract

Dear Editor, This letter provides a simple framework to generalized zero-shot learning for fault diagnosis. For industrial process monitoring, supervised learning and zero-shot learning (ZSL) can only deal with seen and unseen faults, respectively. However, in the online monitoring stage of the actual industrial process, both seen and unseen faults may occur. This makes supervised learning and zero-shot learning impractical in industrial process monitoring. Generalized zero-shot learning (GZSL) can handle this problem, but its implementation process is too complicated. This letter introduces GZSL into industrial process fault diagnosis, and a simple end-to-end framework is provided to implement GZSL-based fault diagnosis. In this framework, GZSL-based fault diagnosis can be realized by using only a binary classification algorithm. Experimental results show that the proposed framework can accomplish this challenging task of GZSL for fault diagnosis.