情态动词
断层(地质)
熵(时间箭头)
算法
模式识别(心理学)
判别式
特征提取
计算机科学
声发射
信号处理
模式(计算机接口)
人工智能
工程类
电子工程
声学
化学
物理
量子力学
数字信号处理
地震学
高分子化学
地质学
操作系统
作者
Yang Li,Meng Xing,Shoune Xiao,Feiyun Xu,Chi-Guhn Lee
标识
DOI:10.1177/14759217231195275
摘要
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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