Non-stationary signal processing for bearing health monitoring
关键词:
摘要: Signals generated by transient vibrations in rolling bearings due to structural defects are non-stationary nature, and reflect upon the operation condition of bearing. Consequently, effective processing signals is critical bearing health monitoring. This paper presents a comparative study four representative time-frequency analysis techniques commonly employed for signal processing. The analytical framework short-time Fourier transform, wavelet packet Hilbert-Huang transform first presented. effectiveness each technique detecting features from time-varying then examined, using an analytically formulated test signal. Subsequently, performance experimentally evaluated, realistic vibration measured system. results demonstrate that selecting appropriate can significantly affect defect identification consequently, improve reliability
sci-hub.se 参考文章(39)
Changting Wang, R.X. Gao, Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring] IEEE Transactions on Instrumentation and Measurement. ,vol. 52, pp. 1296- 1301 ,(2003) , 10.1109/TIM.2003.816807
Marcos Frankowiak, Roger Grosvenor, Paul Prickett, A review of the evolution of microcontroller-based machine and process monitoring International Journal of Machine Tools & Manufacture. ,vol. 45, pp. 573- 582 ,(2005) , 10.1016/J.IJMACHTOOLS.2004.08.018
D.F. Shi, F. Tsung, P.J. Unsworth, Adaptive time-frequency decomposition for transient vibration monitoring of rotating machinery Mechanical Systems and Signal Processing. ,vol. 18, pp. 127- 141 ,(2004) , 10.1016/S0888-3270(03)00085-2
O. Rioul, M. Vetterli, Wavelets and signal processing IEEE Signal Processing Magazine. ,vol. 8, pp. 14- 38 ,(1991) , 10.1109/79.91217
B. Y. Lee, Y. S. Not Available, Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current The International Journal of Advanced Manufacturing Technology. ,vol. 15, pp. 238- 243 ,(1999) , 10.1007/S001700050062
P.C Tseng, A Chou, The intelligent on-line monitoring of end milling International Journal of Machine Tools & Manufacture. ,vol. 42, pp. 89- 97 ,(2002) , 10.1016/S0890-6955(01)00091-8
Shuxin Gu, Jun Ni, Jingxia Yuan, Non-stationary signal analysis and transient machining process condition monitoring International Journal of Machine Tools & Manufacture. ,vol. 42, pp. 41- 51 ,(2002) , 10.1016/S0890-6955(01)00097-9
D. CHEN, W.J. WANG, CLASSIFICATION OF WAVELET MAP PATTERNS USING MULTI-LAYER NEURAL NETWORKS FOR GEAR FAULT DETECTION Mechanical Systems and Signal Processing. ,vol. 16, pp. 695- 704 ,(2002) , 10.1006/MSSP.2002.1488
I. Daubechies, The wavelet transform, time-frequency localization and signal analysis IEEE Transactions on Information Theory. ,vol. 36, pp. 961- 1005 ,(1990) , 10.1109/18.57199
RAJESH GANESAN, TAPAS K. DAS, VIVEKANAND VENKATARAMAN, Wavelet-based multiscale statistical process monitoring: A literature review Iie Transactions. ,vol. 36, pp. 787- 806 ,(2004) , 10.1080/07408170490473060