Bearing Fault Diagnosis Based on Clustering and Sparse Representation in Frequency Domain

Abstract

Bearing is one of the most important transmission components and breaks down more frequently than other parts in rotating equipment. The state detection of the bearing has a great significance for their working performance and safety in industrial production. In this article, we propose a very effective fault diagnosis algorithm based on clustering and sparse representation for rolling bearing. To obtain a robust cluster center for the acquired noisy samples, the samples are first clustered by using their frequency spectrums instead of directly in time-domain waveforms. Then, for each class of samples, an adaptive redundant dictionary is trained over residuals between the original frequency spectrums and their component projected on the calculated cluster center. The noise contained in a test sample is reduced by performing sparse coding and representation with the trained dictionary. Finally, the test sample belonging to a specific category is identified by selecting the maximum cosine similarity value between the reconstructed sample and cluster centers. Experimental results show that the proposed algorithm performs well on both simulated signals and real signals and exhibits advantages over other fault diagnosis methods in terms of diagnosis accuracy.