Fault diagnosis of bearing failure using HMMS

被引：0
|
作者：
Yu T. [1 ]
Chen T. [1 ]
Chen Y. [1 ]
Cheng S. [1 ]
机构：
[1] School of Traffic and Transportation Engineering, Central South University, Changsha
来源：
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology | 2016年 / 48卷 / 02期
关键词：
ACC; Diagnosis; Hidden Markov models; Motor bearings; Pattern recognition;
D O I：
10.11918/j.issn.0367-6234.2016.02.032
中图分类号：
学科分类号：
摘要：
In order to improve the fault diagnosis ability of the system and its accuracy, with previous experience in this article is based on data Rolling health management, this paper presents a novel based on multiple hidden Markov models and Kazuo artificial neural network algorithms and methods of combining ant colony to be used to diagnose and detect bearing faults, which uses HMM and pattern recognition method by combining the bearing vibration signal feature extraction, in the frequency domain analysis of the aging phenomenon, namely the historical data and the new data fault diagnosis and testing, while HMM and ANFIS fault prediction is to estimate the remaining useful life and the life. The experimental results show that the method of HMM and pattern recognition can be used to diagnose and predict the faults. The method can reduce the computational complexity and improve the accuracy of diagnosis, through the different fault diagnosis example elaborates on HMM-based fault diagnosis method effectiveness and feasibility. © 2016, Harbin Institute of Technology. All right reserved.
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页码：184 / 188
页数：4
相关论文
共 14 条
  • [1] Frosini L., Bassi E., Stator current and motor efficiency as indicators for different types of bearing faults in induction motors, IEEE Trans Ind Electron, 57, 1, pp. 244-251, (2010)
  • [2] Immovilli F., Cocconcelli M., Bellini A., Et al., Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals, IEEE Trans Ind Electron, 56, 11, pp. 4710-4717, (2009)
  • [3] Zhang B., Sconyers C., Byington C., Et al., A probabilistic fault detection approach: application to bearing fault detection, IEEE Trans Ind Electron, 58, 5, pp. 2011-2018, (2011)
  • [4] Immovilli F., Bianchini C., Cocconcelli M., Et al., Bearing fault model for induction motor with externally induced vibration, IEEE Trans Ind Electron, 60, 8, pp. 3408-3418, (2013)
  • [5] Yazidi A., Henao H., Capolino G.A., Et al., A webbased remote laboratory for monitoring and diagnosis of ac electrical machines, IEEE Trans Ind Electron, 58, 10, pp. 4950-4959, (2011)
  • [6] Baruah P., Chinnam R.B., HMMs for diagnostics and prognostics in machining processes, Int J Prod Res, 43, 6, pp. 1275-1293, (2005)
  • [7] Rammohan R., Taha M., Exploratory investigations for intelligent damage prognosis using hidden Markov models, Proc 2nd IEEE Conf Systems Man and Cybernetics, pp. 1524-1529, (2005)
  • [8] Chen C., Zhang B., Vachtsevanos G., Machine condition prediction based on adaptive neuro fuzzy and high-order particle filtering, IEEE Trans Ind Electron, 58, 9, pp. 4353-4364, (2011)
  • [9] Strangas E., Aviyente S., Neely J., Et al., Improving the reliability of electrical drives through failure prognosis, Proc 2nd IEEE Conf (SDEMPED), pp. 172-178, (2011)
  • [10] Mahamad A.K., Saon S., Hiyama T., Predicting remaining useful life of rotating machinery based artificial neural network, Comput Math Appl, 60, 4, pp. 1078-1087, (2010)
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