Running State Evaluation Method of Turbine Bearing Based on Feature Vector
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Abstract
Due to the high modal coupling of the cooling turbine bearing in environment control system, it is very difficult to extract the vibration signal feature and construct the recognition model on different feature.Arunning state evaluation method of the turbine bearing is proposed based on the feature vector with limited testing data in this paper. Firstly, aiming at some failure modes in several typical faults of turbine bearing, three time domain feature parameters and seven frequency domain feature parameters are chosen to construct feature vector for discrimination. Then, the feature vectors of different fault testing data are dimensional reduced based on the principal component analysis method. Based on above, the support vector machine (SVM) model of the turbine bearing running state is proposed for monitoring and predicting the occurrence and development of typical turbine bearing failure modes. Experimental results suggest that the bearing running state evaluation method proposed in this paper can improve the prediction accuracy effectively.
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PHM
[2] Jay Lee, Fangji Wu, Wenyu Zhao, Masoud Ghaffari, Linxia Liao, David Siegel. Prognostics and health management design for rotary machinery systems – Reviews, methodology and applications, Mechanical systems and signal processing, 2014,42: 314–334.
[3] Qiu J, Seth BB, Liang SY, Zhang C. Damage mechanics approach for bearing lifetime prognostics, Mechanical systems and signal processing, 2002, 16(5): 817–829.
[4] Chen C, Zhang B, Vachtsevanos G, Orchard M. Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics 2011; 58(9):4353–4364.
[5] Chen C, Zhang B, Vachtsevanos G. Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms. IEEE Transactions on Instrumentation And Measurement 2012; 61(2):297–306.
[6] Liu J, Wang W, Golnaraghi F. An enhanced diagnostic scheme for bearing condition monitoring. IEEE Transactions On Instrumentation And Measurement 2010;59(2): 309–321
[7] Chaochao Chen, George Vachtsevanos. Bearing condition prediction considering uncertainty: An interval type-2 fuzzy neural network approach, Robotics and Computer-Integrated Manufacturing, 28 (2012) 509–516.
[8] Shuai Zhang, Yongxiang Zhang, Lei Li, Jieping Zhu. Rolling elements bearings degradation indicator based on continuous Hidden Markov Model, J Fail. Anal. and Preven, 2015, 15:691–696.
[9] Theodoros H. Loutas, Dimitrios Roulias, George Georgoulas. Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression, IEEE Transactions on Reliability, 2013, 62(4): 821–832.
[10] Abdenour Soualhi, Kamal Medjaher, Noureddine Zerhouni. Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression, IEEE Transactions on Instrumentation and Measurement, 2015, 64(1): 52–62.