Abstract
Sample entropy (SampEn) has been applied in many literatures as a statistical feature to describe the regularity of a time series. However, as components of mechanical system usually interact and couple with each other, SampEn may cause inaccurate or incomplete description of a mechanical vibration signal due to the fact that SampEn is calculated at only one single scale. In this paper, a new method, named multiscale entropy (MSE), taking into account multiple time scales, was introduced for feature extraction from fault vibration signal. MSE in tandem with support vector machines (SVMs) constitutes the proposed intelligent fault diagnosis method. Details on the parameter selection of SVMs were discussed. In addition, performances between SVMs and artificial neural networks (ANNs) were compared. Experiment results verified the proposed model.
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Zhang, L., Xiong, G., Liu, H., Zou, H., Guo, W. (2009). An Intelligent Fault Diagnosis Method Based on Multiscale Entropy and SVMs. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_79
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DOI: https://doi.org/10.1007/978-3-642-01513-7_79
Publisher Name: Springer, Berlin, Heidelberg
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