Affiliations: [a] Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
| [b] School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, China
Correspondence:
[*]
Corresponding author. Xiao Zhao, School of Mechanical Engineering,
Hubei University of Arts and Science, Xiangyang, China.
E-mail: [email protected].
Abstract: The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method.
Keywords: Swarm decomposition, frequency-weighted energy operator, fault diagnosis, rolling
bearing
