Evidential Ensemble Preference-Guided Learning Approach for Real-Time Multimode Fault Diagnosis

Operational changes in industrial production can alter system operating modes, which complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In addition, fault diagnosis tasks encounter the challenge of fault feature drift, which causes a decline in the performance of previously trained models on new data. This article presents a novel approach for real-time multimode fault diagnosis called the evidential ensemble preference-guided approach to tackle these issues. During the offline stage, we extract ensemble preferences of fault information across different operating modes based on the structure of the broad learning system. Subsequently, a parameter iterative update rule is developed that utilizes an evidential reasoning technique to emphasize the preferences during the online stage. The effectiveness of our approach is evaluated by constructing a real-time multimode fault diagnosis dataset using the Tennessee Eastman process and conducting multiple experiments. The results demonstrate that our proposed approach effectively identifies operating modes and diagnoses faults simultaneously, surpassing existing advanced methods.