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Fault diagnosis for machinery based on feature extraction and general regression neural network

  • Haiping Li
  • Jianmin Zhao
  • Xianglong Ni
  • Xinghui Zhang
Original Article
  • 28 Downloads

Abstract

Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a new feature extraction method for signal processing is developed and a general regression neural network (GRNN)—based method is proposed in this paper. Features are extracted from vibration signals that collected from mechanical systems and a feature selection method based on Euclidean distance technique (EDT) is applied. Then, the selected features are processed by the fault characteristic frequencies of mechanical components. And a part of processed data is as train samples and the others as test samples. Finally, the samples are inputted to GRNN to train and verify the model. The proposed method is applied as a fault diagnosis method for both planetary gearbox and bearings datasets, and the performance of it is validated by compared to such methods as radial basis function neural networks (RBFNN), probabilistic neural network (PNN) and a combination model (EMD–EDT). The experimental results show that the GRNN-based method has an advantage over other similar approaches.

Keywords

Feature extraction Signal process Fault diagnosis General regression neural network 

References

  1. Bartelmus W, Zimroz R (2009a) A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mech Syst Signal Process 23:1528–1534CrossRefGoogle Scholar
  2. Bartelmus W, Zimroz R (2009b) Vibration condition monitoring of planetary gearbox under varing external load. Mech Syst Signal Process 23:246–257CrossRefGoogle Scholar
  3. Chen WH, Chen JH, Shao SC (2012) Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units. Int J Control Autom Syst 10:407–414CrossRefGoogle Scholar
  4. Fault data sets (2013) http://www.mfpt.org/FaultData/FaultData.htm. Accessed on 10 April 2013
  5. Fei S (2017) Fault diagnosis of bearing based on wavelet packet transform-phase space reconstruction-singular value decomposition and SVM classifier. Arab J Sci Eng 42:1967–1975CrossRefGoogle Scholar
  6. Feng ZP, Zuo MJ (2012) Vibration signal models for fault diagnosis of planetary gearboxes. J Sound Vib 331:4919–4939CrossRefGoogle Scholar
  7. Feng ZP, Chu FL, Song XG (2004) Application of general regression neural network to vibration trend prediction of rotating machinery. In: International symposium on neural networks, Dalian University of Technology, China, 29 August 2004, 767–772Google Scholar
  8. Feng ZP, Zuo MJ, Chu FL (2010) Application of regularization dimension to gear damage assessment. Mech Syst Signal Process 24:1081–1098CrossRefGoogle Scholar
  9. Jardine AKS, Lin DM, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510CrossRefGoogle Scholar
  10. Jiang YH, Tang BP, Qin Y, Liu WY (2011) Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renew Energy 36:2146–2153CrossRefGoogle Scholar
  11. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearing using machine learning methods. Expert Syst Appl 38:1876–1886CrossRefGoogle Scholar
  12. Kilunda B, Dehombreux P, Chiementin X (2011) Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech Syst Signal Process 25:400–415CrossRefGoogle Scholar
  13. Lebold M, McClintic K, Campbell R et al (2000) Review of vibration analysis methods for gearbox diagnostics and prognostics. In: Proceeding of the 54th meeting of the society for machinery failure prevention technology, Virginia Beach, VA, 1–4 May 2000, pp 623–634Google Scholar
  14. Lee WM, Lim CP, Yuen KK, Lo SM (2004) A hybrid neural network model for noisy data regression. IEEE Trans Syst Man Cybern-Part B: Cybern 34(2):951–960CrossRefGoogle Scholar
  15. Lei YG, Li NP, Lin J, He ZJ (2015) Two new features for condition monitoring and fault diagnosis of planetary gearboxes. J Vib Control 21:755–764CrossRefGoogle Scholar
  16. Li H, Zhang YP, Zheng HQ (2009) Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network. J Mech Sci Technol 23:2780–2789CrossRefGoogle Scholar
  17. Li B, Zhang PL, Wang ZJ et al (2011a) Gear fault detection using multi-scale morphological filters. Measurement 44:2078–2089CrossRefGoogle Scholar
  18. Li B, Zhang PL, Liu DS et al (2011b) Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization. J Sound Vib 330:2388–2399CrossRefGoogle Scholar
  19. Li B, Zhang PL, Mi SS et al (2012) An adaptive morphological gradient lifting wavelet for detecting bearing defects. Mech Syst Signal Process 29:415–427CrossRefGoogle Scholar
  20. Li HP, Zhao JM, Liu J, Ni XL (2016) Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox. J VibroEng 18:5096–5113CrossRefGoogle Scholar
  21. Liu HN, Liu CL, Huang YX (2011) Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech Syst Signal Process 25:558–574CrossRefGoogle Scholar
  22. Lu C, Wang Y, Ragulskis M, Cheng Y (2016) Fault diagnosis for rotating machinery: a method based on image processing. PLoS One 11(10):e0164111CrossRefGoogle Scholar
  23. Polat O, Yildirim T (2007) Recognition of patterns without feature extraction by GRNN. In: International conference on adaptive and natural computing algorithms, Warsaw University of Technology, Poland, 11 April 2007, pp 161–168Google Scholar
  24. Salido JMF, Murakami S (2004) A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery. Appl Soft Comput 4:413–422CrossRefGoogle Scholar
  25. Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18:625–644CrossRefGoogle Scholar
  26. Samuel PD, Pines DJ (2005) A review of vibration-based techniques for helicopter transmission diagnostics. J Sound Vib 282:475–508CrossRefGoogle Scholar
  27. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576CrossRefGoogle Scholar
  28. Su ZY, Zhang YM, Jia MP et al (2011) Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform. J Mech Sci Technol 25:267–272CrossRefGoogle Scholar
  29. Subrahmanya N, Shin YC, Meckl PH (2010) A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics. Mech Syst Signal Process 24:182–192CrossRefGoogle Scholar
  30. Tang XL, Zhuang L, Cai J, Li CB (2010) Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowl-Based Syst 23:486–490CrossRefGoogle Scholar
  31. Tomandl D, Schober A (2001) A modified general regression neural network (MGRNN) with new, efficient training algorithms as a robust ‘black box’-tool for data analysis. Neural Netw 14(4):1023–1034CrossRefGoogle Scholar
  32. Xiong Q, Xu Y, Peng Y et al (2017) Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution. J Mech Sci Technol 31(4):1587–1601CrossRefGoogle Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2018

Authors and Affiliations

  1. 1.Mechanical Engineering CollegeShijiazhuangChina

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