A Novel Fault Diagnosis Method Based on Time-Frequency Image Recognition

Abstract:

A novel intelligent fault diagnosis method based on vibration time-frequency image recognition is proposed in this paper. First, Smooth pseudo Wigner-Ville distribution (SPWVD) is employed to represent the time-frequency distribution characteristics. Then, the features of time-frequency images are extracted by using locality-constrained linear coding (LLC) and spatial pyramid matching. Next, we use the support vector machine to identify these feature vectors for realizing intelligent fault detection. The promise of our algorithm is illustrated by performing above procedures on the vibration signals measured from rolling element bearing with sixteen operating states. Experimental results show that the proposed method can acquire higher diagnosis accuracy compared with the ScSPM method in rolling element bearing diagnosis.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 3569-3573
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.3569
• Online since: November 2014
• Authors: Wei Gang Wang, Zhan Sheng Liu
• Keywords: Intelligent Fault Diagnosis, Locality-Constrained Linear Coding, Rolling Element Bearing, Time-Frequency Image

References

[1] J. Hammond, P. White, The analysis of non-stationary signals using time-frequency methods, Journal Sound and Vibration 190 (1996) 419-447.

DOI: 10.1006/jsvi.1996.0072

[2] L. Atlas, G. Bernard, S. Narayanan, Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors, Proc. IEEE 84 (1996) 1319-1329.

DOI: 10.1109/5.535250

[3] J. Chang, M. Kim, K. Min, Detection of misfire and knock in spark ignition engines by wavelet transform of engine block vibration signals, Measurement Science and Technology 13 (7) (2002) 1108-1114.

DOI: 10.1088/0957-0233/13/7/319

[4] J.D. Wu, J.C. Chen, Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT and E International 39 (4) (2006) 304-311.

DOI: 10.1016/j.ndteint.2005.09.002

[5] C. Wang, Y. Zhang, Z. Zhong, Fault diagnosis for diesel valve trains based on time-frequency images, Mechanical Systems and Signal Processing 22 (8) (2008) 1981-(1993).

DOI: 10.1016/j.ymssp.2008.01.016

[6] J.D. Wu, P.H. Chiang, Application of Wigner-Ville distribution and probability neural network for scooter engine fault diagnosis, Expert Systems with Applications 36 (2 PART 1) (2009) 2187-2199.

DOI: 10.1016/j.eswa.2007.12.012

[7] J.D. Wu, C.K. Huang, An engine fault diagnosis system using intake manifold pressure signal and Wigner-Ville distribution technique, Expert Systems with Applications 38 (1) (2010) 536-544.

DOI: 10.1016/j.eswa.2010.06.099

[8] Y. Li, P.W. Tse, X. Yang, J. Yang, EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine, Mechanical Systems and Signal Processing 24 (1) (2010) 193-210.

DOI: 10.1016/j.ymssp.2009.06.012

[9] Wang J J, Yang J C, Yu K, et al, Locality-constrained Linear Coding for Image Classification, 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2010) 3360-3367.

DOI: 10.1109/cvpr.2010.5540018

[10] Zhang S, Sun F, Liu H, Locality-Constrained Linear Coding with Spatial Pyramid Matching for SAR Image Classification - Springer, Springer Berlin Heidelberg (2014) 867-876.

[11] K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors, PAMI27 (2005) 1615-1630.

[12] K. A. Loparo, Bearing vibration data set, Case Western Reserve University, http: /www. eecs. cwru. edu/laboratory/bearing/download. htm, (2003).

[13] RongEn Fan, Kai-Wei Chang, ChoJui Hsieh, Xiang Rui Wang and Chih-Jen Lin, LIBLINEAR: A Library for Large Linear Classification, Department of Computer Science, National Taiwan University, Taipei 106, Taiwan July 14, (2012).

[14] http: /www. ifp. illi nois. edu/~jya ng29/ScSPM. htm.