The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery

Abstract

The vibration signal containing the fault feature is buried by the strong background noise usually (the noise is referring to the other signals not containing the fault feature in the general sense) when a fault arises in rotating machinery, so the collected vibration signal needs to be processed normally in order to get a correct diagnosis result. Sparse representation theory is a relatively new signal processing method and matching pursuit (MP) is the classical sparse representation method. However, in the traditional MP method only one kind of atom dictionary is constructed to match the fault feature hidden in the vibration signal, which will cause an unsatisfactory analysis result usually when several different kinds of components arise in the vibration signal synchronously. This paper proposes a novel fault feature strengthening method basing on MP to solve the above problem, and the proposed method constructs several different kinds of atom dictionary based on a multi feature pattern set to match the multi-components buried in the collected vibration signal of rotating machinery. Firstly, the effectiveness and advantages of the proposed method are verified by kinds of typical simulation signals of rotating machinery. Then the proposed method is extended to preprocess the vibration signals of bearing and gearbox, and the corresponding analysis results prove that the proposed method has obviously a better fault feature extraction effect on the vibration signal of rotating machinery than the traditional MP method, and the proposed method lays the foundation for further correct fault analysis.

Keywords

Matching pursuit rotating machinery multi feature pattern set feature extraction fault diagnosis

Get full access to this article

View all access options for this article.

References

Antoni

Bonnardot

Raad

et al. (2004) Cyclostationary modeling of rotating machine vibration signals. Mechanical Systems and Signal Processing 18(6): 1285–1314.

Chen

Liu

Dong

et al. (2015) Single-channel bearing vibration signal blind source separation method based on morphological filter and optimal matching pursuit (MP) algorithm. Journal of Vibration and Control 21(9): 1757–1768.

Cui

Yao

Zhang

et al. (2017) Application of pattern recognition in gear faults based on the matching pursuit of characteristic waveform. Measurement 104: 212–222.

Ding

Zhao

Miao

(2018) Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection. Mechanical Systems and Signal Processing 111: 399–422.

Elia

Cocconcelli

Mucchi

et al. (2017) Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings. Proceedings of the Institution of Mechanical Engineers, Part C – Journal of Mechancial Engineering Science 231(6): 1113–1128.

Feng

Liang

(2016) Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning. Measurement 90: 382–395.

Feng

Zhou

Zuo

(2017) Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples. Measurement 103: 106–132.

Ding

Lin

(2016) Gearbox coupling modulaiton separation method based on matching pursuit and correlation filtering. Mechanical Systems and Signal Processing 66–67: 597–611.

Inalpolat

Kahraman

(2009) A theoretical and experimental investigation of modulation sidebands of planetary gear sets. Journal of Sound and Vibration 323(3–5): 677–696.

10.

Liu

Huang

(2011) Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mechanical Systems and Signal Processing 25(2): 558–574.

11.

Liu

Jia

et al. (2017) Application of EMD–WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction. Journal of Vibroengineering 19(3): 1793–1808.

12.

Lybeck N, Marble S and Morton B (2007) Validating prognostic algorithms: A case study using comprehensive bearing fault data. In: 2007 IEEE Aerospace Conference Proceedings, Big Sky, Montana, USA, 3–10 March 2007, pp.1–9. Piscataway, NJ: Institute of Electrical and Electronics Engineers.

13.

Nese

Kilic

Akinci

(2012) Analysis of wind turbine blade deformation with STFT method. Energy Education Science and Technology, Part A – Energy Science and Research 29(1): 679–686.

14.

Qin

Mao

Tang

(2013) Vibration signal component separation by iteratively by using basis pursuit and its application in mechanical fault detection. Journal of Sound and Vibration 332(20): 5217–5235.

15.

Randall

Antoni

Chobsaard

(2001) The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing 15(5): 945–962.

16.

Starck

Elad

Donoho

(2014) Redundant multisacle transforms and their application for morphological component separation. Advances in Imaging and Electron Physics 132: 287–348. .

17.

Tang

Chen

Dong

(2014) Sparse representation based latent components analysis for machinery weak fault detection. Mechanical Systems and Signal Processing 46(2): 373–388.

18.

Tran

Muttil

Perera

BJC

(2016) Enhancing accuracy of autoregressive time series forecasting with input selection and wavelet transformation. Journal of Hydroinformatics 18(5): 791–802.

19.

Wang

Gan

Zhu

(2017) Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit. Journal of Intelligent Manufacuring 28(6): 1377–1391.

20.

Sun

Krishnappa

(2000) Bearing diagnostics based on pattern recognition of statistical parameters. Journal of Vibration and Control 6(3): 375–392.

21.

Zhou

Chen

Dong

et al. (2016) Detection and diagnosis of bearing fautls using shift-invariant dictionary learning and hidden Markov model. Mechanical Systems and Signal Processing 72–73: 65–79.