基于CELCD和MFVPMCD的智能故障诊断方法研究

doi:10.3969/j.issn.0372-2112.2017.03.006

电子学报 ›› 2017, Vol. 45 ›› Issue (3): 546-551.DOI: 10.3969/j.issn.0372-2112.2017.03.006

基于CELCD和MFVPMCD的智能故障诊断方法研究

潘海洋¹, 郑近德¹, 杨宇², 童宝宏¹

1. 安徽工业大学机械工程学院, 安徽马鞍山 243032;
2. 湖南大学汽车车身先进设计制造国家重点实验室, 湖南长沙 410082

收稿日期:2015-06-18 修回日期:2015-09-17 出版日期:2017-03-25
- 作者简介:
- 潘海洋 男,1989年生于安徽宿州,现为安徽工业大学机械工程学院教师.主要研究方向为设备状态监测与故障诊断、信号处理.E-mail:pansea@sina.cn;郑近德 男,1986年生于安徽阜阳,现为安徽工业大学机械工程学院教师.主要研究方向为设备状态监测与故障诊断、信号处理.E-mail:lqdlzheng@126.com
- 基金资助:
- 国家自然科学基金 (No.51505002，No.51175158）; 安徽高校自然科学研究项目资助 (No.2015A080）

Research on Combined Intelligent Fault Diagnostic Method Based on CELCD and MFVPMCD

PAN Hai-yang¹, ZHENG Jin-de¹, YANG Yu², TONG Bao-hong¹

1. School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, Anhui 243032, China;
2. State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, Hunan 410082, China

Received:2015-06-18 Revised:2015-09-17 Online:2017-03-25 Published:2017-03-25
- Supported by:
- National Natural Science Foundation of China (No.51505002, No.51175158); Natural Science Research Program of Colleges and Universities in Anhui Province (No.2015A080)

摘要/Abstract

摘要：

针对旋转机械故障诊断方法中信号处理和模式识别的不足，即端点效应和判别片面性问题，提出一种基于互相关匹配延拓局部特征尺度分解（Cross-correlation matching endpoint Extension Local Characteristic scale Decomposition，CELCD）和改进多变量预测模型（Variable Predictive Model based Class Discriminate，VPMCD）的智能故障诊断方法，首先探索待分解信号前后端的数据规律，选取匹配波形完成端点延拓，然后利用局部特征尺度分解（Local Characteristic scale Decomposition，LCD）得到各去除端点效应的内禀尺度分量（Intrinsic Scale Component，ISC），最后输入到基于多模型融合的多变量预测模型（Multi-model Fusion-Variable Predictive Model based Class Discriminate，MFVPMCD）分类器中进行概率状态判定.实验分析结果表明，所提方法能有效地对滚动轴承的工作状态进行识别.

Abstract:

To suppress end effects of signal processing and judgment contingency of pattern recognition in the rotating machinery fault diagnosis method,an intelligent fault diagnosis method is proposed based on the cross-correlation matching endpoint extension local characteristic scale decomposition (CELCD) and the improved variable predictive model based class discriminate (VPMCD).Firstly,the characteristic of the decomposed signal is explored and the matched waveform is selected to complete the endpoint extension.Then the extension waveform is decomposed by the local characteristic scale decomposition (LCD),at the same time,and the intrinsic scale components (ISCs) with removed endpoint effect are obtained.Finally,the features of each ISC are extracted and input to the multi-model fusion-variable predictive model based class discriminate (MFVPMCD) classifier for the judgment of state probability.Experimental results show that the proposed method can effectively identify the running state of roller bearing.

Key words: cross-correlation matching endpoint extension, local characteristic scale decomposition (LCD), multi-model fusion, variable predictive model based class discriminate (VPMCD), fault diagnosis

中图分类号:

TH113

潘海洋, 郑近德, 杨宇, 等. 基于CELCD和MFVPMCD的智能故障诊断方法研究[J]. 电子学报, 2017, 45(3): 546-551.

PAN Hai-yang, ZHENG Jin-de, YANG Yu, et al. Research on Combined Intelligent Fault Diagnostic Method Based on CELCD and MFVPMCD[J]. Acta Electronica Sinica, 2017, 45(3): 546-551.

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基于CELCD和MFVPMCD的智能故障诊断方法研究

Research on Combined Intelligent Fault Diagnostic Method Based on CELCD and MFVPMCD

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