Abstract
This paper demonstrates the methodology of fault classification of rotating machinery using support vector machine (SVM) in combination with genetic algorithm and particle swarm optimization. In order to detect the machine health condition, classifier uses the features as the inputs from the preprocessed raw signal of a machine. Support vector machine classifier prepared in combination of hybrid adaptive particle swarm optimization and adaptive genetic algorithm (HAPAG) proposed for proficient flaw detection. An industrial case study of a centrifugal pump is considered and the data is given for both training and testing of the classifier. A similar study with comparable existing fault classifiers on the identification triumph is investigated. SVM based HAPAG system results in clustering the various faults with more than 90 % accuracy when compared with adaptive tuning of SVM based techniques like SVM—adaptive particle swarm optimization and SVM—adaptive genetic algorithm. The outcome indicates the adequacy of choosing the classifiers in finding the machine health condition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aihong J, Lizhe Y (2010) Fault diagnosis based on adaptive genetic algorithm and BP neural network. In international conference on computer engineering and technology (ICCET), Jodhpur Institute of Engineering & Technology, pp. 427–430
Antoni J, Randall RB (2002) Differential diagnosis of gear and bearing faults. J Vib Acoust 124:165–171
Boser B, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, New York
Dalpiaz G, Rivola A, Rubini R (2000) Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mech Syst Signal Process 14(3):387–412
Dellomo MR (1999) Helicopter gearbox fault detection: a neural network based approach. J Vib Acoust 121:265–272
Fei S, Liu C, Zeng Q, Miao Y (2008). Application of particle swarm optimization based support vector machine in fault diagnosis of turbo-generator, In: intelligent information technology application, second international symposium, pp. 1040–1044
Gang Xu (2013) An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput 219(9):4560–4569
Grimmelius HT, Woud JK, Been G (1995) On-line failure diagnosis for compression refrigeration plants. Int J Refrig 18:31–41
Huang KY (2011) A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowl Based Syst 24(3):420–426
Leung SYS, Tang Yang, Wong WK (2012) A hybrid particle swarm optimization and its application in neural networks. Expert Syst Appl 39(1):395–405
Li Youwen, Bao Demei, Luo Cun et al (2012) An intelligent fault diagnosis method for oil-immersed power transformer based on adaptive genetic algorithm. Adv Autom Robot 1:155–162
Martin KF (1994) A review by discussion of condition monitoring and fault diagnosis in machine tools. Int J Mach Tools Manuf 34(4):527–551
McFadden PD (2000) Detection of gear faults by decomposition of matched differences of vibration signals. Mech Syst Signal Process 14:805–817
Randall RB (Guest Ed.) (2001) Special issue on gear and bearing diagnostics. Mech Syst Signal Process 15(5): 317–328
Roemer MJ, Kacprzynski GJ, Nwadiogbu EO, Bloor G (2001) Development of diagnostic and prognostic technologies for aerospace health management applications. In: Proceedings of the IEEE Aerospace Conference, Big Sky, pp. 3139–3147
Samanta B, Nataraj C (2009) Use of particle swarm optimization for machinery fault detection. Eng Appl Artif Intell 22(2):308–316
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(7–8):657–665
Shiroishi J, Li Y, Liang S, Kurfess T, Danyluk S (1997) Bearing condition diagnostics via vibration and acoustic emission measurements. Mech Syst Signal Process 11(5):693–705
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Widodo Achmad, Yang Bo-Suk (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574
Yang Bo-Suk, Tran VT (2012) An intelligent condition based maintenance platform for rotating machinery. Expert Syst Appl 39(3):2977–2988
Acknowledgments
The authors would like to thank the professors of GITAM University for all the support and reviewers for their valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kishore, B., Satyanarayana, M.R.S. & Sujatha, K. Efficient fault detection using support vector machine based hybrid expert system. Int J Syst Assur Eng Manag 7 (Suppl 1), 34–40 (2016). https://doi.org/10.1007/s13198-014-0281-y
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-014-0281-y