Composite-Graph-Based Sparse Subspace Clustering for Machine Fault Diagnosis

Abstract

Sparse subspace clustering (SSC) is an effective method to cluster sensing signal for fault diagnosis in mechanical systems. SSC is based on a global expression strategy describing each data point by other data points from all the potential clusters. A drawback of this strategy is that it generates nonzero elements in the nondiagonal blocks of the similarity matrix, thereby reducing the performance of the matrix for data discrimination. To improve SSC's performance, a composite-graph-based SSC (CG-SSC) method is developed by introducing distance among the data points into SSC, where the L1-norm of the sparse coefficients is replaced by the product of the data distance and the sparse coefficients. The distance serves as a constraint to the amplitude of the sparse coefficients. A sparse coefficient with small amplitude is assigned if the distance between the expressed data and the expressing data is large, and vice versa. This method supports the construction of a compact cluster. The effectiveness of the presented method is experimentally verified by data measured on several bearings and gearboxes with different types of faults. The method is also compared with classical clustering methods, and the results indicate its advantage for data sorting and clustering.