Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems

DOI: 10.4236/jsip.2012.31002   PDF   HTML     5,000 Downloads   8,019 Views   Citations


Previously, fault diagnosis of fixed or steady state mechanical failures (e.g., pumps in nuclear power plant turbines, engines or other key equipment) applied spectrum analysis (e.g., fast Fourier transform, FFT) to extract the frequency features as the basis for identifying the causes of failure types. However, mechanical equipment for increasingly instant speed variations (e.g., wind turbine transmissions or the mechanical arms used in 3C assemblies, etc.) mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems.

Share and Cite:

C. Wang and Y. Kang, "Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems," Journal of Signal and Information Processing, Vol. 3 No. 1, 2012, pp. 16-25. doi: 10.4236/jsip.2012.31002.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. F. Xu and G. K. Li, “Practical Wavelet Method,” Huazhong University of Science & Technology Press Co., Taichung, 2004, pp. 46-48.
[2] G. Dennis, “Theory of Communications,” Journal of the Institution of Electrical Engineers, Vol. 93, No. 4, 1946, pp. 429-457.
[3] F. Al-Badourl, L. Chedect and M. Suna, “Non-Stationary Vibration Signal Analysis of Rotating Machinery via Time-Frequency and Wavelet Techniques,” Information Sciences Signal Processing and Their Applications, Kuala Lumpur, 10-13 May 2010, pp. 21-24.
[4] Z. H. Wang and Y. H. Yu, “Fault Diagnosis of Diesel Engine Exhaust Valve Leakage Using Time Frequency Analysis Method,” Journal of Wuhan University of Technology, Vol. 32, No. 4, 2008, pp. 645-648.
[5] F. F. Bie, F. T. Wang and F. X. Lu, “Intelligent Diagnosis of Reciprocating Compressor Based on Local Wave Time-Frequency Spectra Processing,” Transactions of the Chinese Society for Agricultural Machinery, Vol. 38, No. 9, 2007, pp. 159-162.
[6] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, Vol. 323, No. 6088, 1986, pp. 533-536. doi:10.1038/323533a0
[7] H. C. Kuo, L. J Wu and J. H. Chen, “Neural-Fuzzy Fault Diagnosis in a Marine Propulsion Shaft System,” Journal of Materials Processing Technology, Vol. 122, No. 1, 2000, pp. 12-22. doi:10.1016/S0924-0136(01)01157-8
[8] C. C. Wang, Y. Kang, P. C. Shen, Y. P. Chang and Y. L. Chung, “Applications of Fault Diagnosis in Rotary Machinery by Using Time Series Analysis with Neural Network,” Expert System With Application, Vol. 37, No. 2, 2010, pp. 1696-1702. doi:10.1016/j.eswa.2009.06.089

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.