  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.
