Abstract
As an efficient tool, support vector machine (SVM) has advantage on over-fitting problem and the small-samples cases. For improving the classifier’s performance, extraction feature is required. The fast fixed-point independent component analysis (FastICA) is applied for feature extraction. The parameters of SVM are optimized by particle swarm optimization (PSO). In this paper, an integrated framework of FastICA and PSO-SVM algorithm for fault diagnosis is presented. Compared with other predictors, this model has greater generality ability and higher accuracy.
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References
Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM (2003) A comparison of PCA, KPCA and ICA for dimensional reduction in support vector machine. Neurocomputing 55:321–336
Cheung Y, Xu L (1999) An empirical method to select dominant independent component in ICA for time series analysis. Neurocomputing 41:145–152
Cheung Y, Xu L (2001) Independent component ordering in ICA time series analysis. Neurocomputing 41:145–152
Demetgul M, Tansel IN, Taskin S (2009) Fault diagnosis of pneumatic systems with artificial neural network algorithms. Expert Syst Appl 36(7):10512–10519
Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput chem Eng 17(3):245–255
Ge HW, Liang YC, Zhou Y, Guo XC (2005) A particle swarm optimization-based algorithm for job-shop scheduling problem. Int J Comput Methods 3:419–430
Girolami M (1991) Self-organising neural networks: independent component analysis and blind source separation. Springer, London
Hyvärinen A (1999) Fast and robust fixed point algorithms for independent component analysis. IEEE Trans Neural Networks 10(3):626–634
Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Networks 13:411–430
Isermann R (2006) Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer, Berlin, pp 287–293
Jutten C, Herault J (1991) Blind separation of sources, Part I: an adaptive algorithm based on neuromimetic architecture. Signal Process 24:1–10
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, pp 1942–1948
Koldovský Z, Tichavský P, Oja E (2006) Efficient variant of algorithm FastICA for independent component analysis attaining the Cramér-Rao lower bound. IEEE Trans Neural Networks 17(5):1265–1277
Lecun Y, Jackel LD, Bottou L et al (1995) Learning algorithms for classification: a comparison on handwritten digit recognition. In: Kwon JH, Cho S (eds) Neural network: the statistical mechanics perspective. World Scientific, Singapore, pp 261–276
Lee T (1998) Independent component analysis: theory and applications. MA dissertation, Kluwer Academic, USA
Lin S-W, Ying K-C, Chen S-C, Lee Z-J (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35:1817–1824
Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16(7):657–665
Vapnik V (1999a) The nature of statistical learning theory. Springer, New York
Vapnik V (1999b) An overview of statistical learning theory. IEEE Trans Neural Network 10(5):988–999
Venkatasubramanian V, Rengaswamy R, Kavuri SN, Yin K (2003) A review of process fault detection and diagnosis (Part III): process history based methods. Comput Chem Eng 27(3):327–346
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Yin, J., He, Z., Ruan, Yp. (2013). Fault Diagnosis Based on Fast Independent Component Analysis and Optimized Support Vector Machines. In: Qi, E., Shen, J., Dou, R. (eds) International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38445-5_71
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DOI: https://doi.org/10.1007/978-3-642-38445-5_71
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