航发轴承复合故障诊断的循环维纳滤波方法

doi:10.19665/j.issn1001-2400.2022.06.017

摘要/Abstract

摘要：

航空发动机主轴轴承故障诊断是发动机预测性维护与健康管理的重要组成部分。针对航发主轴轴承复合故障诊断难度大,易受其它振动干扰信号影响的问题,提出了一种基于规范相关分析盲信号提取和循环维纳滤波的航发主轴轴承复合故障诊断方法。通过优化规范相关分析准则,提出自适应共轭梯度算法实现盲信号提取,结合故障特征频率利用盲信号提取算法从观测信号中提取故障特征信号,将提取到的故障特征信号作为循环维纳滤波的期望信号,以此设计循环维纳滤器来恢复故障源信号。通过分析滤波后信号的包络谱,完成轴承复合故障的诊断。提出的方法克服了现有方法过分依赖轴承参数,且利用固定的数学模型获得的期望信号过于理想化而无法应用于实际工程的问题。分别利用仿真数据和试验机实测数据,验证了算法对主轴轴承复合故障诊断的有效性。

关键词: 循环维纳滤波器, 滚动轴承, 故障诊断, 复合故障, 包络谱

Abstract:

Fault diagnosis of an aero-engine spindle bearing is an important part of engine prognostics and health management.As is known,the diagnosis of the compound fault of an aero-engine spindle bearing is very difficult and easily affected by other vibration interference signals.We present a compound fault diagnosis method of an aero-engine spindle bearing based on blind signal extraction of canonical correlation analysis (CCA) and cyclic Wiener filtering.First,an adaptive conjugate gradient algorithm is proposed for extracting the blind signal by optimizing CCA criterion.Then,combined with the fault feature frequency,the blind signal extraction algorithm is used to extract the fault feature signal from the observed signal.The extracted fault fe12ature signal is regarded as the expected response of the cyclic Wiener filter.Finally,the cyclic Wiener filter is designed to recover the fault signal,and the envelope spectrum of the filtered signal is analyzed to complete the diagnosis of the bearing composite fault.The proposed algorithm overcomes the problem that the existing methods rely on the bearing parameters too much,and that the expected signal obtained from the fixed mathematical model is too ideal to be applied to practical engineering.Both simulated data and experimental data are used to verify the effectiveness of the algorithm in compound fault diagnosis.

Key words: cyclic wiener filter, rolling bearing, fault diagnosis, compound faults, envelop spectrum

中图分类号:

TP206

张伟涛,纪晓凡,黄菊,楼顺天. 航发轴承复合故障诊断的循环维纳滤波方法[J]. 西安电子科技大学学报, 2022, 49(6): 139-151.

ZHANG Weitao,JI Xiaofan,HUANG Ju,LOU Shuntian. Cyclic wiener filtering for compound fault diagnosis of an aero-engine rolling element bearing[J]. Journal of Xidian University, 2022, 49(6): 139-151.

图/表 16

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

表2

图11

图12

图13

图14

参考文献 23

[1]	ROBERT B, JEROME A. RollingElement Bearing Diagnostics-A tutorial[J]. Mechanical Systems & Signal Processing, 2011, 25(2):485-520.
[2]	SMITH W A, RANDALL ROBERT R B. RollingElement Bearing Diagnostics Using the Case Western Reserve University Data:A Benchmark Study[J]. Mechanical Systems & Signal Processing, 2015, 64/65:100-131.
[3]	ANTONI J, BONNARDOT F, RAAD A, et al. Cyclostationary Modelling of Rotating Machine Vibration Signals[J]. Mechanical Systems & Signal Processing, 2004, 18(6):1285-1314.
[4]	王国彪, 何正嘉, 陈雪峰, 等. 机械故障诊断基础研究“何去何从”[J]. 机械工程学报, 2013, 49(1):63-72.
	WANG Guobiao, HE Zhengjia, CHEN Xuefeng, et al. Basic Research on Machinery Fault Diagnosis—What is the Prescription[J]. Journal of Mechanical Engineering, 2013, 49(1):63-72.
[5]	贺王鹏, 孙伟, 苏博, 等. 机械故障诊断的稀疏特征提取方法[J]. 西安电子科技大学学报, 2018, 45(2):154-159.
	HE Wangpeng, SUN Wei, SU Bo, et al. Sparse Feature Extraction Technique and Its Applications to Machinery Fault Diagnosis[J]. Journal of Xidian University, 2018, 45(2):154-159.
[6]	陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报, 2020, 56(17):91-107. doi: 10.3901/JME.2020.17.091
	CHEN Shiqian, PENG Zhike, ZHOU Peng. Review of Signal Decomposition Theory and Its Applications in Machine Fault Diagnosis[J]. Journal of Mechanical Engineering, 2020, 56(17):91-107. doi: 10.3901/JME.2020.17.091
[7]	万志国, 贺王鹏, 廖楠楠, 等. 齿根裂纹与齿面剥落故障的振动响应机理研究[J]. 西安电子科技大学学报, 2021, 48(6):131-137.
	WAN Zhiguo, HE Wangpeng, LIAO Nannan, et al. Study on the Vibration Response Mechanism of Gear Root Crack and Spalling[J]. Journal of Xidian University[J], 2021, 48(6):131-137.
[8]	WANG Y X, LIANG M. Identification ofMultiple Transient Faults Based on the Adaptive Spectral Kurtosis Method[J]. Journal of Sound and Vibration, 2011, 331:470-486. doi: 10.1016/j.jsv.2011.08.029
[9]	姚德臣, 杨建伟, 程晓卿, 等. 基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J]. 机械工程学报, 2018, 54(9):168-176. doi: 10.3901/JME.2018.09.168
	YAO Dechen, YANGJianwei, CHENG Xiaoqing, et al. Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier[J]. Journal of Mechanical Engineering, 2018, 54(9):168-176. doi: 10.3901/JME.2018.09.168
[10]	程军圣, 于德介, 杨宇. 基于内禀模态奇异值分解和支持向量机的故障诊断方法[J]. 自动化学报, 2006, 32(3):476-480.
	CHENG Junsheng, YU Dejie, YANG Yu. Fault Diagnosis Method Based on Intrinsic Modal Singular Value Decomposition and Support Vector Machine[J]. Acta Automatica Sinica, 2006, 32(3):476-480.
[11]	高明哲, 许爱强, 唐小峰. 基于多核多分类相关向量机的模拟电路故障诊断方法[J]. 自动化学报, 2019, 45(2):203-213.
	GAO Mingzhe, XU Aiqiang, TANG Xiaofeng. Analog Circuit Fault Diagnosis Method Based on Multi-core and Multiclassiflcation Relevant Vector Machine[J]. Acta Automatica Sinica, 2019, 45(2):203-213.
[12]	HAN T, ZHANG L W, YIN Z J, et al. Rolling Bearing Fault Diagnosis with Combined Convolutional Neural Networks and Support Vector Machine[J/OL].[2021-12-01].DOI:10.1016/j.measurement.2021.109022. doi: 10.1016/j.measurement.2021.109022
[13]	雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5):94-104. doi: 10.3901/JME.2018.05.094
	LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era[J]. Journal of Mechanical Engineering, 2018, 54(5):94-104. doi: 10.3901/JME.2018.05.094
[14]	毛文涛, 田思雨, 窦智, 等. 一种基于深度迁移学习的滚动轴承早期故障在线检测方法[J]. 自动化学报, 2022, 48(1):302-314.
	MAO Wentao, TIAN Siyu, DOU Zhi, et al. An Online Detection Method for Early Faults of Rolling Bearings Based on Deep Transfer Learning[J]. Acta Automatica Sinica, 2022, 48(1):302-314.
[15]	张建勋, 杜党波, 司小胜, 等. 基于最后逃逸时间的随机退化设备寿命预测方法[J]. 自动化学报, 2022, 48(1):249-260.
	ZHANG Jianxun, DU Dangbo, SI Xiaosheng, et al. Life Prediction Method for Stochastic Degraded Equipment Based on Last Escape Time[J]. Acta Automatica Sinica:, 2022, 48(1):249-260.
[16]	沈长青, 汤盛浩, 江星星, 等. 独立自适应学习率优化深度信念网络在轴承故障诊断中的应用研究[J]. 机械工程学报, 2019, 55(7):81-88. doi: 10.3901/JME.2019.07.081
	SHENChangqing, TANG Shenghao, JIANG Xingxing, et al. Bearings Fault Diagnosis Based on Improved Deep Belief Network by Self-individual Adaptive Learning Rate[J]. Journal of Mechanical Engineering, 2019, 55(7):81-88. doi: 10.3901/JME.2019.07.081
[17]	XUE F, ZHANG W M, XUE F, et al. A Novel Intelligent Fault Diagnosis Method of Rolling Bearing Based on Two-Stream Feature Fusion Convolutional Neural Network[J/OL].[2021-12-02].DOI:10.1016/j.measurement.2021.109226. doi: 10.1016/j.measurement.2021.109226
[18]	MAO W T, CHEN J X, LIANG X H, et al. A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2):443-456. doi: 10.1109/TIM.2019.2903699
[19]	LI X, ZHANG W, DING Q. Understanding andImproving Deep Learning-Based Rolling Bearing Fault Diagnosis with Attention Mechanism[J]. Signal Processing, 2019, 161:136-154. doi: 10.1016/j.sigpro.2019.03.019
[20]	明阳, 陈进, 董广明. 基于循环维纳滤波器和包络谱的轴承故障诊断[J]. 振动工程学报, 2010, 23(5):537-540.
	MING Yang, CHENJin, DONG Guangming. Rolling bearing fault diagnosis based on cyclic Wiener filter and envelop spectrum[J]. Journal of Vibration Engineering, 2010, 23(5):537-540.
[21]	张峰, 马舒啸, 石现峰. 基于循环维纳滤波的振动信号去噪算法研究[J]. 计算机技术与发展, 2014, 24(6):49-51.
	ZHANG Feng, MAShuxiao, SHI Xianfeng. Research on Denoising Algorithm of Vibration Signal Based on Cricular Wiener Filtering[J]. Computer Technology and Development, 2014, 24(6):49-51.
[22]	郝芳, 王宏超. 改进循环维纳滤波器算法的滚动轴承复合故障诊断[J]. 中国工程机械学报, 2018, 16(4):371-376.
	HAO Fang, WANGHongchao. Fault Diagnosis of Rolling Element Bearing'compound Faults Basing on Improved Cyclic Wiener Filter Algorithm[J]. Chinese Journal of Construction Machinery, 2018, 16(4):371-376.
[23]	Case Western Reserve University, Bearing Data Center, Seeded Fault Test Data[EB/OL]. http://engineering.case.edu/bearingdaacenter/. 2016.

参数组别	转速/(r·min^-1)	接触角/(°)
1	1 000	29.5
2	1 100	29.5