Multi-gate Mixture-of-Expert Combined with Synthetic Minority Over-sampling Technique for Multimode Imbalanced Fault Diagnosis

Compared with traditional multivariate statistical techniques, deep neural networks have been frequently used for single-mode fault diagnosis and have shown promising results. However, in the real world, a complex industrial process may have several modes and fewer fault samples than normal. Although several multivariate statistical techniques focus on multimode fault diagnosis, those methods usually identify modes and then locally diagnose faults while ignoring relative information across modes and data imbalance problems. In this paper, a deep learning-based method combining Multi-gate Mixture-of-Experts (MMOE) and Synthetic Minority Over-sampling Technique (SMOTE) is proposed to address multimode imbalance fault diagnosis. Specifically, MMOE can identify modes and diagnose faults in each mode simultaneously. Furthermore, it investigates the common information of multiple modes by sharing network parameters. Moreover, SMOTE can address the fault imbalance problem by over-sampling minority fault samples. The experiment results show the performance improvements of multimode imbalance fault diagnosis using MMOE-SMOTE on the Tennessee Eastman (TE) Chemical benchmark process.