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Fault diagnosis is a crucial area of research in industry. Industrialprocesses exhibit diverse operating conditions, where data often havenon-Gaussian, multi-mode, and center-drift characteristics. Data-drivenapproaches are currently the main focus in the field, but continuous faultclassification and parameter updates of fault classifiers pose challenges formultiple operating modes and real-time settings. Thus, a pressing issue is toachieve real-time multi-mode fault diagnosis in industrial systems. In thispaper, a novel approach to achieve real-time multi-mode fault diagnosis isproposed for industrial applications, which addresses this critical researchproblem. Our approach uses an extended evidence reasoning (ER) algorithm tofuse information and merge outputs from different base classifiers. These baseclassifiers based on broad learning system (BLS) are trained to ensure maximumfault diagnosis accuracy. Furthermore, pseudo-label learning is used to updatemodel parameters in real-time. The effectiveness of the proposed approach isdemonstrated on the multi-mode Tennessee Eastman process dataset.

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