基于AR模型和谱熵的自适应小波包络检测

摘要/Abstract

摘要： 针对传统故障诊断的包络问题，提出了一种基于自回归（auto regressive,AR）模型和谱熵的自适应复解析小波包络检测方法。通过AR模型从数据内在规律性上剔除机械振动信号中可线性预测的平稳成分，提取共振衰减的非平稳成分，在不同频带下进行复解析小波包络，结合谱熵在频域内与通带滤波的相关性选定最佳包络。仿真和试验数据分析结果表明，该方法能有效地提取故障特征频率，较传统方法自适应性更强，鲁棒性更高，包络效果更好，在工程应用中具有良好的前景。

关键词: 自回归预测, 小波变换, 谱熵, 包络检测

Abstract: For the envelope problems of traditional fault diagnosis, a method of adaptive complex analytic wavelet envelope detection was proposed based on AR model and spectral entropy herein. The method eliminated the stationary components for linear prediction from the mechanical vibration signals by AR model, and extracted the non-stationary components of resonance damping. The generated signals were enveloped by complex analytic wavelet in different frequency bands, the best envelope was selected based on the correlation between the spectral entropy and the band-pass filter in the frequency domain. This method owns higher adaptivity, better robustness and envelope effectiveness than that of the traditional one. Thus it has favorable prospect in engineering applications.

Key words: auto regressive (AR) prediction, wavelet transform, spectral entropy, envelope detection

中图分类号:

TH17
TP274

何翔, 高宏力, 郭亮, 吴远昊. 基于AR模型和谱熵的自适应小波包络检测[J]. 中国机械工程.

HE Xiang, GAO Hongli, GUO Liang, WU Yuanhao. Adaptive Wavelet Envelope Detection Based on AR Model and Spectral Entropy[J]. China Mechanical Engineering.

参考文献

[1]梅宏斌.滚动轴承振动监测与诊断——理论·方法·系统[M].北京:机械工业出版社,1996.
MEI Hongbin. Vibration Monitoring and Diagnosis of Rolling Bearing [M].Beijing: China Machine Press, 1996.
[2]ANTONI J. The Spectral Kurtosis: a Useful Tool for Characterizing Non-stationary Signals [J].Mechanical System and Signal Processing, 2006,20(2):282-307.
[3]张根保,范秀君.基于蚁群算法优化选取阈值的EMD消噪方法[J].中国机械工程,2014,25(4):427-432.
ZHANG Genbao, FAN Xiujun. Method for De-nosing Based on Optimal Threshold of EMD Optimized by Ant Colony Algorithm[J]. China Mechanical Engineering, 2014, 25(4): 427-432.

[4]吴文兵,黄宜坚.基于故障诊断的双谱优良特性体现[J]. 中国机械工程,2014,25(6):771-775.
WU Wenbing, HUANG Yijian. Presence of Bispectrums Prior Performance Based on Fault Diagnosis [J].China Mechanical Engineering, 2014, 25(6):771-775.
[5]何岭松,李巍华.用Morlet小波进行包络检波分析[J].振动工程学报,2002,15(1):119-122.
HE lingsong, LI Weihua. Morlet Wavelet and Its Application in Enveloping [J]. Journal of Vibration Engineering, 2002, 15(1):119-122.
[6]郭瑜,郑华文,高艳，等.基于谱峭度的滚动轴承包络分析研究[J].振动、测试与诊断, 2011,31(4):517-521.
GUO Yu, ZHENG Huawen, GAO Yan, et al. Envelope Analysis of Rolling Bearings Based on Spectral Kurtosis [J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4):517-521.
[7]廖庆斌,李舜酩.一种旋转机械振动信号特征提取的新方法[J].中国机械工程,2006,17(16):1675-1679.
LIAO Qingbin, LI Shunming. A Novel Method Feature Extraction Rotating Machinery Vibration Signals [J]. China Mechanical Engineering, 2006,17(16):1675-1679.
[8]CHENG J S, YU D J, YANG Y. A Fault Diagnosis Approach for Roller Bearings Based on EMD Method and AR Model[J].Mechanical Systems and Signal Processing,2006,20(2):350-362.
[9]从飞云,陈进,董广明.基于谱峭度和AR模型的滚动轴承故障诊断[J].振动、测试与诊断,2012,32(4):538-541.
CONG Feiyun, CHEN Jin, DONG Guangming. Spectral Kurtosis and AR Model Based Method for Fault Diagnosis of Rolling Bearings [J]. Journal of Vibration, Measurement & Diagnosis, 2012, 32(4):538-541.
[10]高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报(自然科学版),2011,41(5):998-1001.
GAO Qingqing, JIA Minping. EEMD Method Based Singular Value Spectral Entropy in Fault Diagnosis of Rotating Machinery [J]. Journal of Southeast University(Natural Science Edition), 2011, 41(5):998-1001.

[1]	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.
[2]	刘增华1,2;张婷婷1,2;苏瑞祥1,2;陈洪磊1,2;何存富1,2;吴斌1,2. 基于十字形阵列和2D-MUSIC算法的Lamb波检测[J]. 中国机械工程, 2020, 31(17): 2038-2044.
[3]	戴稳1;张超勇1;孟磊磊1;薛燕社1;肖鹏飞1;尹勇2. 采用深度学习的铣刀磨损状态预测模型[J]. 中国机械工程, 2020, 31(17): 2071-2078.
[4]	宁静;冉伟;种传杰;陈春俊. 基于S变换的高速列车小幅蛇行识别方法[J]. 中国机械工程, 2019, 30(09): 1097-1102,1110.
[5]	段晨东;张荣. 基于改进经验小波变换的机车轴承故障诊断[J]. 中国机械工程, 2019, 30(06): 631-637.
[6]	张玉存1;张喜英1;付献斌2;张桂茹3;徐飞1. 基于小波与双三次插值的高温锻件红外图像增强方法[J]. 中国机械工程, 2017, 28(17): 2095-2099.
[7]	王友仁, 陈伟, 孙灿飞, 孙权, 黄海安. 基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断[J]. 中国机械工程, 2017, 28(12): 1484-1490.
[8]	洪涛, 赵佃云. 高速摄像环境下航天电连接器分离边缘提取算法[J]. 中国机械工程, 2017, 28(09): 1074-1078.
[9]	何志坚, 周志雄. 基于FSWT细化时频谱SVD降噪的冲击特征分离方法[J]. 中国机械工程, 2016, 27(09): 1184-1190.
[10]	赵海峰, 张亚, 李世中, 郭燕. 侵彻弹体频率特性分析及过载信号处理[J]. 中国机械工程, 2015, 26(22): 3034-3039.
[11]	钟先友, 赵春华, 田红亮, 陈保家, 赵美云. 基于自适应最大相关峭度解卷积和频率切片小波变换的齿轮故障特征提取[J]. 中国机械工程, 2014, 25(21): 2880-2885.
[12]	罗颂荣, 程军圣, 杨宇. 多尺度高阶奇异谱熵和GA-VPMCD方法在转子故障智能诊断中的应用[J]. 中国机械工程, 2014, 25(21): 2912-2917,2924.
[13]	任志英;高诚辉;林建兴;申丁. 基于双树复小波变换的三维粗糙表面评定方法研究[J]. 中国机械工程, 2014, 25(13): 1795-1799.
[14]	周雁冰1, 柳亦兵1, 李宏2, 滕伟1, 李状1. 基于双谱熵的齿轮裂纹故障特征提取[J]. 中国机械工程, 2013, 24(02): 190-194.
[15]	吴强, 孔凡让, 何清波, 刘永斌, 李鹏. 基于小波变换和ICA的滚动轴承早期故障诊断[J]. 中国机械工程, 2012, 23(7): 835-840.