Multivariate variational mode decomposition and generalized composite multiscale permutation entropy for multichannel fault diagnosis of hoisting machinery system

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-09-20 DOI:10.1177/14759217231195275
Yang Li, Xiangyin Meng, Shide Xiao, Feiyun Xu, Chi-Guhn Lee
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Abstract

Due to the harsh working environment of hoisting machinery system, the fault information of the important components is significantly complex, which leads to the fault signals not being collected completely by using only single channel. To alleviate this problem, acoustic emission (AE) experiments are applied to collect multichannel AE signal of hoisting machinery system. Additionally, a new intelligent fault diagnosis method based on multivariate variational mode decomposition (MVMD) and generalized composite multiscale permutation entropy (GCMPE) is proposed to extract multichannel AE fault features and implement multichannel fault diagnosis of hoisting machinery system. Firstly, based on variational mode decomposition (VMD) and the idea of multichannel AE data processing, MVMD is proposed to process the original multichannel AE signals collected from hoisting machinery system, which can obtain adaptively several multichannel modal components containing discriminative information. Meanwhile, GCMPE is presented to extract the fault information of multichannel modal components obtained by MVMD, which can improve the feature extraction performance of the original multiscale permutation entropy. The experimental results demonstrate the effectiveness and superiority of the proposed method in multichannel fault diagnosis of hoisting machinery system compared with some traditional single-channel analysis and other multichannel analysis methods.
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基于多通道变分模态分解和广义复合多尺度置换熵的起重机械系统故障诊断
由于起重机械系统工作环境恶劣,重要部件的故障信息非常复杂,仅使用单一通道无法完全采集故障信号。为了解决这一问题,采用声发射实验对起重机械系统的多通道声发射信号进行采集。此外,提出了一种基于多变量变分模态分解(MVMD)和广义复合多尺度置换熵(GCMPE)的智能故障诊断方法,提取多通道声发射故障特征,实现起重机械系统的多通道故障诊断。首先,基于变分模态分解(VMD)和多通道声发射数据处理思想,提出了对起重机械系统采集的原始多通道声发射信号进行处理的变分模态分解方法,该方法可以自适应地获得多个包含判别信息的多通道模态分量;同时,提出了GCMPE对MVMD得到的多通道模态分量进行故障信息提取,提高了原始多尺度排列熵的特征提取性能。实验结果表明,与传统的单通道分析方法和其他多通道分析方法相比,该方法在起重机械系统多通道故障诊断中的有效性和优越性。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
期刊最新文献
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