Affiliations: [a]
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China
| [b]
Department of Mechanical Engineering, Universidad Politécnica Salesiana, Cuenca, Ecuador
| [c] CEOT, Universidade do Algarve, Faro, Portugal
| [d]
Control Systems Department-CEMISID, Universidad de Los Andes, Mérida, Venezuela
Correspondence:
[*]
Corresponding author. Chuan Li, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China. Tel.: +86 23 6276 8469; Fax: +86 23 62769374; E-mail: chuanli@21cn.com.
Abstract: Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults.
Keywords: Rolling element bearing, fuzzy clustering, fuzzy comprehensive evaluation, fault detection, envelope demodulation
