Feature Selection With Partial Autoencoding for Zero-Sample Fault Diagnosis

Abstract

Fault diagnosis is still a challenging task es- pecially for unseen faults, which could happen in the sys- tems. Zero-sample fault diagnosis alleviates this issue by utilizing information from seen faults and attributes defined by domain knowledge. However, existing methods suffer from irrelevant features in the original space while existing supervised feature selection methods yield unsatisfactory performance due to discrepancy caused by domain differ- ence. In this paper, we propose a novel feature selection method for zero-sample fault diagnosis, called concrete partial autoencoder. The concrete partial autoencoder se- lects features beneficial for both seen and unseen faults through striking a balance between classification accuracy and reconstruction errors of selected features. The con- crete partial autoencoder utilizes categorical reparameter- ization to efficiently solve the feature selection problem. The evaluation results on the Tennessee Eastman Process show that the proposed method improves classification ac- curacy and robustness against irrelevant features at zero- sample fault diagnosis.