Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

TL;DR

This paper introduces a knowledge space sharing model with generation and discrimination mechanisms to improve generalized zero-shot industrial fault diagnosis, effectively addressing domain shift and unseen fault detection challenges.

Contribution

The proposed KSS model innovatively combines sample generation and discrimination to enhance zero-shot fault diagnosis, outperforming existing methods on benchmark data.

Findings

KSS outperforms state-of-the-art methods on Tennessee-Eastman benchmark.

The generation mechanism effectively creates rare fault samples.

Discrimination mechanism reduces misclassification of unseen faults.

Abstract

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem.