柴油机多元信号自适应分解方法比较

顾程; 乔新勇; 靳莹; 韩立军

doi:10.3969/j.issn.1001-2222.2020.06.014

车用发动机 ›› 2020, Vol. 0 ›› Issue (6) : 83-89. DOI: 10.3969/j.issn.1001-2222.2020.06.014

栏目

柴油机多元信号自适应分解方法比较

顾程¹，乔新勇¹，靳莹¹，韩立军²

作者信息 +

Comparison of Adaptive Decomposition Methods for Multivariate Signal of Diesel Engine

GU Cheng¹，QIAO Xinyong¹，JIN Ying¹，HAN Lijun²

Author information +

文章历史 +

摘要

针对单一信号通道反映故障信息不全面、不准确的问题，提出利用多元变分模态分解（MVMD）处理多通道信号提取故障特征，实现故障诊断。首先通过构建多分量调制仿真信号，分析比较MEMD、NAMEMD和MVMD的分解效果，然后利用MVMD对柴油机4个通道振动信号进行自适应分解，提取每层分量的能量分布作为故障特征，最后利用支持向量机对不同失火故障进行了识别。结果表明，MVMD在抑制模态混叠和分解效率上均优于其他两种算法，且能够有效识别柴油机不同类型失火故障。

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.

导出引用

顾程，乔新勇，靳莹，韩立军. 柴油机多元信号自适应分解方法比较[J]. 车用发动机. 2020, 0(6): 83-89 https://doi.org/10.3969/j.issn.1001-2222.2020.06.014

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