基于LMD和ABC优化KELM的故障诊断方法

doi:10.3969/j.issn.1007-7375.2022.03.015

工业工程 ›› 2022, Vol. 25 ›› Issue (3): 124-131.doi: 10.3969/j.issn.1007-7375.2022.03.015

基于LMD和ABC优化KELM的故障诊断方法

杨静宗, 施春朝, 杨天晴, 吴丽玫

保山学院大数据学院，云南保山 678000

收稿日期:2020-11-27 发布日期:2022-07-06
通讯作者: 施春朝(1988—)，男，云南省人，讲师，硕士，主要研究方向为机器学习。Email：120455539@qq.com E-mail:120455539@qq.com
作者简介:杨静宗(1991—)，男，云南省人，副教授，博士，主要研究方向为复杂工业过程的控制、检测与优化
基金资助:
云南省地方本科高校(部分)基础研究联合专项资金资助项目(202001BA070001-109); 保山学院校级科学研究资助项目(ZKMS202101)

A Research on Fault Diagnosis Method Based on LMD and ABC Optimized KELM

YANG Jingzong, SHI Chunchao, YANG Tianqing, WU Limei

School of Big Data, Baoshan University, Baoshan 678000, China

Received:2020-11-27 Published:2022-07-06

摘要/Abstract

摘要： 针对高压隔膜泵单向阀振动信号的非平稳非线性特性，提出一种基于局部均值分解(LMD)、排列熵和人工蜂群算法优化核极限学习机(KELM)的故障诊断方法。采用LMD将振动信号分解成多个分量信号，通过互相关准则选取关联度较大的分量信号，并求出相应的排列熵作为特征向量。输入经过人工蜂群算法优化的KELM中构建故障诊断模型。通过对实际工况下采集的不同故障状态信号的处理分析，结果显示利用该方法对单向阀的运行状况进行故障诊断不但能够较好地表征信号的状态信息，且故障识别准确率达到95.65%。同时，与采用传统的KELM、ELM相比有着更高的识别准确率。

关键词: 单向阀, 局部均值分解, 排列熵, 人工蜂群算法, 故障诊断

Abstract: Aiming at the non-stationary and non-linear characteristics of vibration signal of check valve in high pressure diaphragm pump, a fault diagnosis method based on local mean decomposition (LMD), permutation entropy and artificial bee colony (ABC) optimization kernel extreme learning machine (KELM) is proposed. The vibration signal is decomposed into multiple component signals by LMD, and then the component signal with higher correlation degree is selected by cross-correlation criterion, and the corresponding permutation entropy is calculated as the eigenvector. The fault diagnosis model is built in KELM optimized by artificial bee colony algorithm. Through the processing and analysis of different fault state signals collected under the actual working conditions, the results show that the fault diagnosis method for the operation status of one-way valve can not only better represent the signal state information, but the fault recognition accuracy also reaches 95.65%. At the same time, compared with traditional KEML and ELM, it has higher recognition accuracy.

Key words: check valve, local mean decomposition, permutation entropy, artificial bee colony algorithm, fault diagnosis

中图分类号:

TP18

杨静宗, 施春朝, 杨天晴, 吴丽玫. 基于LMD和ABC优化KELM的故障诊断方法[J]. 工业工程, 2022, 25(3): 124-131.

YANG Jingzong, SHI Chunchao, YANG Tianqing, WU Limei. A Research on Fault Diagnosis Method Based on LMD and ABC Optimized KELM[J]. Industrial Engineering Journal, 2022, 25(3): 124-131.

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基于LMD和ABC优化KELM的故障诊断方法

A Research on Fault Diagnosis Method Based on LMD and ABC Optimized KELM

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