Affiliations: [a] College of Electrical and Information Engineering, Quzhou University, Quzhou, People’s Republic of China | [b] Logistics Engineering College, Shanghai Maritime University, Shanghai, People’s Republic of China
Abstract: Compressive sensing retains the substantive characteristics of the original signal based on several sampling values and can realize distortionless reconstruction of signals with high probability. Hence, compressive sensing solves the contradictions among massive sampling data, signal processing speed and hardware equipment in the framework of traditional Shannon’s sampling theorem. In this study, as a means of compressive sensing, a new failure recognition method of rolling bearing based on the characteristic parameters of compressed data and fuzzy-C mean (FCM) clustering is proposed. In this method, kurtosis, variance and waveform factor are used as the characteristic parameters. Using the proposed method, the sensitive characteristics were extracted by using the compressed information directly, and the eigenvectors of the sample signals were classified and identified through FCM clustering. In the experiment, the proposed method was compared with different compressed matrix methods and the traditional signal acquisition methods under the same axis diameter and different axis diameters. The results demonstrated the improved performance of the proposed method compared to the other methods.
