Affiliations: School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
Correspondence:
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Corresponding author. Huaqing Wang, School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China. E-mail: [email protected].
Abstract: The prediction of performance degradation is significant for the health monitoring of rolling bearing, which helps to greatly reduce the loss caused by potential faults in the entire life cycle of rotating machinery. As a new method of machine learning based on statistical learning theory, a so-called multivariable least squares support vector machines (LS-SVM) was developed. However, it is unsatisfactory for the prediction of performance degradation without adequate consideration of time variation and data volatility, which are notable features of the obtained time series signal from bearings. To overcome these problems, a new multivariable LS-SVM with a moving window over time slices is proposed. In this model, different features over time slices are extracted through a moving window to construct new sample pairs according to the embedding theory. The model adaptability is also improved through an iterative updating strategy. Furthermore, the algorithm parameters are optimized using coupled simulated annealing to improve the prediction accuracy. Bearing fault experiments show that the proposed model outperforms the general multivariable LS-SVM.
Keywords: Multivariable least squares support vector machines, performance degradation prediction, time slices, moving window
