Adaptive multi-scale method for the non-linear dynamic feature extraction of mechanical vibration signals

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (14) : 224-232.

LIU Min1， FAN Hongbo1, ZHANG Yingtang1， LI Zhining1， YANG Wangcan2

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Abstract

Aiming at the fault feature extraction of mechanical vibration signals, a feature extraction method based on the independent variational mode decomposition(VMD) and multi-scale nonlinear dynamic parameters was put forward.The spectral cyclic coherence coefficient was proposed to select the matching waveform which was used to complete the endpoint extension for the mechanical vibration signal.The extended signal was decomposed into some intrinsic mode functions (IMFs) in different frequency scales by using the VMD.The effective IMFs were selected according to the cross-correlation criterion and the independent components with effective frequency band were separated from the effective IMFs by using the kernel independent component analysis.The composite multi-scale fuzzy entropy partial mean of each independent IMF was calculated.The orthogonal transform was used to orthogonalize independent IMFs to construct a multi-dimensional hyperbody, and its volume was used to define and calculate the dual measure fractal dimension of the vibration signal.Thereby,the multi-scale nonlinear dynamic parameters were obtained to achieve mechanical fault diagnosis.The simulation and experimental results show that the proposed method can effectively suppress the end effect and mode mixing in the VMD,which improves the effect of signal decomposition; the feature parameters have higher classification accuracy, which greatly improves the accuracy of mechanical fault diagnosis.

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spectral cyclic coherence coefficient / endpoint extension / independent variational mode decomposition / composite multi-scale fuzzy entropy partial mean / dual measure fractal dimension

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LIU Min1， FAN Hongbo1, ZHANG Yingtang1， LI Zhining1， YANG Wangcan2. Adaptive multi-scale method for the non-linear dynamic feature extraction of mechanical vibration signals[J]. Journal of Vibration and Shock, 2020, 39(14): 224-232

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