Vibration source model estimation and state specificity perception of a rotor structure

Ma, Sai, Li, Shun Ming, Liu, Hai Long, Miao, Xiao Dong, Wany, Yong, Liu, Tian Wen and Xiong, Yeping (2015) Vibration source model estimation and state specificity perception of a rotor structure. Journal of Vibration Engineering & Technologies, 3 (5), 575-587.

Abstract

Rotor structures in abnormal states will create non-stationary vibration sources, these non-stationary features are often ignored by traditional signal processing algorithms, thereby the determination of rotor running states is affected. A new state specificity perception method based on kernel density function estimation combined with higher moments feature extraction and multi-class support vector machine is proposed. Firstly, for rotors with non-stationary vibration source, kernel density estimation algorithm is used to estimate the probability density model, which fully considers band broadening features of non-stationary signals. Then higher order moments of non-stationary vibration source model are calculated, the values are assembled to state mark vectors. Finally, the multi-class support vector machine is used to classify the state mark vectors. Three types of abnormal states are set on cascade rotor system for experiments, acceleration data is collected and the state specificity perception process is carried out. All abnormal states are identified successfully. High accuracy proves the effectiveness of Kernel Density Estimation-Support Vector Machine (KDE-SVM) method. The proposed specificity extraction and perception method provides new ideas for running structure condition monitoring.

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Contributors

Author: Yeping Xiong

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