Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine

Abstract

This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs the probability of each fault mode occurring. First, multiple two-class sub-classifiers based on SBELM are trained by using single-fault samples to extract the correlation between various pairs of single-fault, and the sub-classifiers are integrated with the proposed hierarchical multi-label classification (HMLC) strategy to form the diagnostic model based on HMLC-SBELM. Then, samples of single faults and simultaneous faults are used to generate the optimal discriminative thresholds by using optimization algorithms. Finally, the probabilistic output generated by the HMLC-SBELM-based model is transformed into the final fault modes by using the optimal discriminative threshold. The model performance is evaluated by using actual vibration signals of the main reducer and is compared with several classical models. The contrastive results indicate that the proposed model is more accurate, efficient, and stable.