Comparison of Adaptive Decomposition Methods for Multivariate Signal of Diesel Engine

GU Cheng; QIAO Xinyong; JIN Ying; HAN Lijun

doi:10.3969/j.issn.1001-2222.2020.06.014

Vehicle Engine ›› 2020, Vol. 0 ›› Issue (6) : 83-89. DOI: 10.3969/j.issn.1001-2222.2020.06.014

Comparison of Adaptive Decomposition Methods for Multivariate Signal of Diesel Engine

GU Cheng¹，QIAO Xinyong¹，JIN Ying¹，HAN Lijun²

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Abstract

For the problems of incomplete and inaccurate fault information of univariate signal, multivariate variational modal decomposition (MVMD) was proposed to process multi-channel signals to extract fault features and hence realize fault diagnosis. A multi-component modulation simulation signal was constructed and the decomposition effects of MEMD, NAMEMD and MVMD were analyzed and compared. The adaptive decomposition of the vibration signals from the four channels for diesel engine was then conducted by using MVMD and the energy distribution of each IMF component was extracted as the fault feature. Finally, different misfire faults were identified by support vector machine. The results show that MVMD is better than the other two algorithms in suppressing modal aliasing and decomposition efficiency and can effectively identify different misfire faults of diesel engine.

Key words

multivariate variational modal decomposition (MVMD) / vibration signal / fault diagnosis

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GU Cheng，QIAO Xinyong，JIN Ying，HAN Lijun. Comparison of Adaptive Decomposition Methods for Multivariate Signal of Diesel Engine[J]. Vehicle Engine. 2020, 0(6): 83-89 https://doi.org/10.3969/j.issn.1001-2222.2020.06.014