基于优选小波包与马氏距离的滚动轴承性能退化GRU预测

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摘要为了有效地描述滚动轴承的性能退化趋势，提出基于优选小波包与马氏距离的滚动轴承性能退化门控循环单元(Gated Recurrent Unit, GRU)预测新方法。利用小波包分解法提取滚动轴承数据的能量特征，其中考虑到不同的分解层数和小波基函数会影响特征提取的效果，建立了最佳分解层数原则，并根据能量波动变化率选择最优小波基函数。然后，通过计算特征向量间的马氏距离(Mahalanobis Distance, MD)作为滚动轴承性能退化的指标。最后，将该指标作为GRU的输入来构造退化趋势的预测模型，并运用实测的滚动轴承全寿命数据进行验证。实验结果表明，该方法可以获得准确的预测结果，有利于设备维护人员更好的掌握滚动轴承的退化趋势。

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关键词 ：趋势预测, 优选小波包, 马氏距离(MD), 门控循环单元(GRU)

Abstract：To effectively describe performance degradation trend of rolling bearings, a new method based on optimal wavelet packet and Mahalanobis distance (MD) for rolling bearing performance degradation gated recurrent unit (GRU) prediction was proposed. The wavelet packet method was used to extract energy features of rolling bearing data considering effects of decomposition layers and wavelet basis functions on effect of feature extraction, and establish the principle of optimal decomposition layer number. According to the variation rate of energy fluctuation, the optimal wavelet basis function was selected. Then, MDs among feature vectors were calculated, and these MDs were taken as bearing performance degradation indexes. Finally, these indexes were taken as input of GRU to construct a prediction model of degradation trend. The actually measured rolling bearing full life data were used to verify the prediction model. Results showed that the proposed method can gain correct prediction results; it is beneficial for equipment maintenance personnel to better grasp degradation trend of rolling bearings.

Key words： trend prediction optimal wavelet packet Mahalanobis distance (MD) gated recurrent unit (GRU)

收稿日期: 2019-03-15 出版日期: 2020-08-28

引用本文:

郑小霞，钱轶群，王帅. 基于优选小波包与马氏距离的滚动轴承性能退化GRU预测[J]. 振动与冲击, 2020, 39(17): 39-46.
ZHENG Xiaoxia, QIAN Yiqun, WANG Shuai. GRU prediction forperformance degradation of rolling bearings based on optimalwavelet packet and Mahalanobis distance. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(17): 39-46.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I17/39

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