A novel feature extraction and optimisation method for fault diagnosis by combining WPT with FCM

Insight: Non-Destructive Testing and Condition Monitoring

Volumn 58, Issue 1, 2016, Pages 35-41

  Lu, Wenqing ^a   Zhang, Laibin ^a   Liang, Wei ^a   Yu, Xuchao ^a  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

Fault diagnosis; Feature extraction and optimisation; Fuzzy c means; Support vector machine; Wavelet packet transform

Indexed keywords

EXTRACTION; FAILURE ANALYSIS; FEATURE EXTRACTION; FUZZY SYSTEMS; IMAGE RETRIEVAL; PACKET NETWORKS; PATTERN RECOGNITION; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; VECTORS; WAVELET TRANSFORMS;

COMPUTATION QUANTITY; EXPERIMENT PLATFORMS; EXPERIMENTAL VERIFICATION; FUZZY C MEAN; OPTIMISATIONS; ROTOR FAULT DIAGNOSIS; WAVELET PACKET TRANSFORM(WPT); WAVELET PACKET TRANSFORMS;

FAULT DETECTION;

EID: 84954165070     PISSN: 13542575     EISSN: 17544904     Source Type: Journal    
DOI: 10.1784/insi.2016.58.1.35     Document Type: Article

References (18)

1. J M Spyers-Ashby, P G Bain et al, 'A comparison of fast Fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data', Journal of Neuroscience Methods, Vol 83 (1), pp 35-43, 1998.
2. B Vishwash, P S Pai et al, 'Multiscale slope feature extraction for gear and bearing fault diagnosis using wavelet transform', Procedia Materials Science, Vol 5, pp 1650-1659, 2014.
3. M Kang and J M Kim, 'Singular value decomposition-based feature extraction approaches for classifying faults of induction motors', Mechanical Systems and Signal Processing, Vol 41 (1), pp 348-356, 2013.
4. R Ricci and P Pennacchi, 'Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions', Mechanical Systems and Signal Processing, Vol 25 (3), pp 821-838, 2011.
5. D Bhalla, R K Bansal et al, 'Function analysis-based rule extraction from artificial neural networks for transformer incipient fault diagnosis', Electrical Power and Energy Systems, Vol 43 (1), pp 1196-1203, 2012.
6. F Chen, B Tang et al, 'Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimisation', Measurement, Vol 47, pp 576-590, 2014.
7. A A Yusuff, C Fei et al, 'Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression', Electric Power Systems Research, Vol 81 (7), pp 1258-1265, 2011.
8. P Boskoski and D Juricic, 'Fault detection of mechanical drives under variable operating conditions based on wavelet packet Renyi entropy signatures', Mechanical Systems and Signal Processing, Vol 31, pp 369-381, 2012.
9. G S Hu, F F Zhu et al, 'Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines', Expert Systems with Applications, Vol 35 (1), pp 143-149, 2008.
10. R R Coifman and M V Wickerhauser, 'Entropy-based algorithms for best basis selection', IEEE Transactions on Information Theory, Vol 38 (2), pp 713-718, 1992.
11. C E Shannon, 'A mathematical theory of communication', ACM SIGMOBILE Mobile Computing and Communications Review, Vol 5 (1), pp 3-55, 2001.
12. S Ekici, S Yildirim et al, 'Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition', Expert Systems with Applications, Vol 34 (4), pp 2937-2944, 2008.
13. J Wu and C Liu, 'An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network', Expert System with Application, Vol 36 (3), pp 4278-4286, 2009.
14. C B James, E Robert et al, 'FCM: The fuzzy c-means clustering algorithm', Computers & Geosciences, Vol 10 (2), pp 191-203, 1984.
15. W Huang, S K Oh et al, 'Design of hybrid radial basis function neural networks (HRBFNNs) realised with the aid of hybridisation of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)', Neural Networks, Vol 60, pp 166-181, 2014.
16. L J Wang, K Sawada et al, 'Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy', Computers & Geosciences, Vol 57, pp 81-92, 2013.
17. S I Amari and S Wu, 'Improving support vector machine classifiers by modifying kernel functions', Neural Networks, Vol 12 (6), pp 783-789, 1999.
18. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: A library for support vector machines, 2001. Software available at: Http://www.csie.ntu.edu.tw/~cjlin/libsvm