基于自适应自相关谱峭度图的滚动轴承故障诊断方法

doi:10.3969/j.issn.1004-132X.2021.07.003

中国机械工程 ›› 2021, Vol. 32 ›› Issue (07): 778-785，792.DOI: 10.3969/j.issn.1004-132X.2021.07.003

基于自适应自相关谱峭度图的滚动轴承故障诊断方法

郑近德1，2;王兴龙1;潘海洋1;童靳于1;刘庆运1

1.安徽工业大学机械工程学院，马鞍山，243032
2.安徽理工大学矿山智能装备与技术安徽省重点实验室，淮南，232001

出版日期:2021-04-10 发布日期:2021-04-16
作者简介:郑近德，男，1986年生，副教授。研究方向为设备状态监测与故障诊断、非平稳信号处理、模式识别、非线性动力学。E-mail:lqdlzheng@126.com。
基金资助:
国家重点研发计划（2017YFC0805100）；
国家自然科学基金（51975004）；
安徽省高校自然科学研究重点项目（KJ2019A0053,KJ2019A092）；
安徽理工大学矿山智能装备与技术安徽省重点实验室开放基金(201902005)

Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram

ZHENG Jinde1,2;WANG Xinglong1;PAN Haiyang1;TONG Jinyu1;LIU Qingyun1

1.School of Mechanical Engineering,Anhui University of Technology,Maanshan,Anhui,243032
2. Anhui Key Laboratory of Mine Intelligent Equipment and Technology,Anhui University of Science & Technology,Huainan,Anhui,232001

Online:2021-04-10 Published:2021-04-16

摘要/Abstract

摘要： 自相关谱峭度图通过最大重叠离散小波包变换对信号频谱进行分割，并选取最大峭度值所对应频带内的信号进行诊断分析。针对自相关谱峭度图方法在分割频带时因遵循二叉树结构而导致的频带划分区域固定问题，提出一种基于自适应自相关谱峭度图方法的滚动轴承故障诊断方法。自适应自相关谱峭度图方法以改进的经验小波变换为基础，对原始信号傅里叶谱进行包络与平滑处理后再分割，实现了自相关谱峭度图方法自适应分割频带的目的。通过仿真信号与实验数据分析，并将所提方法与快速谱峭度及自相关谱峭度图方法进行对比，结果表明，所提出方法能够准确地检测到合适的解调频带，同时其故障特征更加明显。

关键词: 自相关谱峭度图, 改进经验小波变换, 滚动轴承, 故障诊断

Abstract: In Autogram method, the signal spectrum was divided by the maximum overlap discrete wavelet packet transform, and the signals in the frequency band corresponding to the maximum kurtosis value were selected for diagnostic analysis. However, the method followed the binary tree structure when the frequency band was divided, and the division area of this structure was fixed. A fault diagnosis method of rolling bearings was proposed based on adaptive Autogram to solve this problem. The improved empirical wavelet transform was used as the basis of adaptive Autogram. In this process, the original signal Fourier spectrum was enveloped and smoothed and then segmented, thus achieving the purpose of frequency band was adaptively divided by Autogram. The simulation signals and experimental data were analyzed through the proposed method, and the analysis results were compared with the existing fast kurtogram and Autogram. The results show that the optimal demodulation frequency band may be accurately detected by the proposed method, and the fault characteristics are more obvious.

Key words: Autogram, improved empirical wavelet transform, rolling bearing, fault diagnosis

中图分类号:

TH165.3

郑近德, 王兴龙, 潘海洋, 童靳于, 刘庆运. 基于自适应自相关谱峭度图的滚动轴承故障诊断方法[J]. 中国机械工程, 2021, 32(07): 778-785，792.

ZHENG Jinde, WANG Xinglong, PAN Haiyang, TONG Jinyu, LIU Qingyun. Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram[J]. China Mechanical Engineering, 2021, 32(07): 778-785，792.

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基于自适应自相关谱峭度图的滚动轴承故障诊断方法

Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram

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