Composite Fault Diagnosis of Rolling Bearings: A Feature Selection Approach Based on the Causal Feature Network

A rolling bearing is a complex system consisting of the inner race, outer race, rolling element, etc. The interaction of components may lead to composite faults. Selecting the features that can accurately identify the fault type from the composite fault features with causality among components is key to composite fault diagnosis. To tackle this issue, we propose a feature selection approach for composite fault diagnosis based on the causal feature network. Based on the incremental association Markov blanket discovery, we first use the algorithm to mine the causal relationships between composite fault features and construct the causal feature network. Then, we draw upon the nodes’ centrality indicators in the complex network to quantify the importance of composite fault features. We also propose the criteria for threshold selection to determine the number of features in the optimal feature subset. Experimental results on the standard dataset for composite fault diagnosis show that our approach of using the causal relationship between features and the nodes’ centrality indicators of complex network can effectively identify the key features in composite fault signals and improve the accuracy of composite fault diagnosis. Experimental results thus verify our approach’s effectiveness.