An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System

TL;DR

This paper introduces a real-time multi-mode fault diagnosis method for industrial systems using broad learning systems and evidence reasoning, effectively handling diverse operating conditions and data characteristics.

Contribution

It proposes a novel approach combining broad learning systems, evidence reasoning, and pseudo-label learning for real-time fault diagnosis across multiple modes.

Findings

Effective on Tennessee Eastman dataset

Achieves high fault diagnosis accuracy

Supports real-time multi-mode operation

Abstract

Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the main focus in the field, but continuous fault classification and parameter updates of fault classifiers pose challenges for multiple operating modes and real-time settings. Thus, a pressing issue is to achieve real-time multi-mode fault diagnosis in industrial systems. In this paper, a novel approach to achieve real-time multi-mode fault diagnosis is proposed for industrial applications, which addresses this critical research problem. Our approach uses an extended evidence reasoning (ER) algorithm to fuse information and merge outputs from different base classifiers. These base classifiers based on broad learning system (BLS) are trained to ensure maximum fault diagnosis accuracy. Furthermore, pseudo-label learning is used to update model parameters in real-time. The effectiveness of the proposed approach is demonstrated on the multi-mode Tennessee Eastman process dataset.