Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

Entropy

Bin Chen²  Zhaoli Yan¹ 

[1] Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China; E-Mail:;School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China; E-Mail:

关键词: wheel-bearing;    defective frequency;    spectral kurtosis;    time-spectral kurtosis;    feature extraction;   

DOI  :  10.3390/e16010607

来源: mdpi

【 摘 要 】

Wheel-bearings easily acquire defects due to their high-speed operating conditions and constant metal-metal contact, so defect detection is of great importance for railroad safety. The conventional spectral kurtosis (SK) technique provides an optimal bandwidth for envelope demodulation. However, this technique may cause false detections when processing real vibration signals for wheel-bearings, because of sparse interference impulses. In this paper, a novel defect detection method with entropy, time-spectral kurtosis (TSK) and support vector machine (SVM) is proposed. In this method, the possible outliers in the short time Fourier transform (STFT) amplitude series are first estimated and preprocessed with information entropy. Then the method extends the SK technique to the time-domain, and extracts defective frequencies from reconstructed vibration signals by TSK filtering. Finally, the multi-class SVM was applied to classify bearing defects. The effectiveness of the proposed method is illustrated using real wheel-bearing vibration signals. Experimental results show that the proposed method provides a better performance in defect frequency detection and classification than the conventional SK-based envelope demodulation.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland