显式动力学驱动的轴承变工况故障诊断方法

doi:10.3969/j.issn.1004-132X.2023.08.013

摘要/Abstract

摘要： 实际工程应用中，旋转机械通常处于变工况状态，不同工况样本特征分布的差异限制了故障诊断模型的应用范围。为解决变工况下特征分布不一致的问题，提出了显式动力学驱动的轴承变工况故障诊断方法。首先，采用有限元法仿真多工况、多故障类型的样本，通过冗余属性投影得到不同故障类型下的仿真投影矩阵；然后，利用仿真投影矩阵同步消除实测信号特征样本的复杂工况冗余信息；最后，结合支持向量机进行轴承变工况的故障诊断。通过两组变工况轴承数据集对所提方法进行验证，验证结果表明，仿真投影矩阵有效消除了冗余工况信息，投影后的故障诊断准确率达到100%。

关键词: 滚动轴承, 有限元分析, 冗余属性投影, 故障诊断

Abstract: In practical engineering applications， rotating machines were usually in variable conditions， and the feature distribution of samples in different conditions was differentiated， which limited the application scope of fault diagnosis models. In order to solve the problem of inconsistent feature distribution under variable conditions， a method of bearing failure diagnosis of variable conditions under explicit dynamics was proposed. Firstly， the finite element method was used to simulate sample of multiple conditions and multiple fault types， and the simulation projection matrix under different fault types were obtained by NAP. Secondly， the complex working condition nuisance information of the measured signal feature samples were eliminated simultaneously by simulation projection matrix. Finally， the bearing fault diagnosis was performed based on support vector machine. The proposed method was validated by two sets of variable condition bearing data sets. The test results show that the simulation projection matrix may effectively eliminate the nuisance condition information， and the accuracy of fault diagnosis after projection reach 100%.

Key words: rolling bearing, finite element analysis, nuisance attribute projection（NAP）, fault diagnosis

中图分类号:

TH133.3

张龙, 张号, 王朝兵, 王晓博, 文培田, 赵丽娟. 显式动力学驱动的轴承变工况故障诊断方法[J]. 中国机械工程, 2023, 34(08): 982-992.

ZHANG Long, ZHANG Hao, WANG Chaobing, WANG Xiaobo, WEN Peitian, ZHAO Lijuan. Explicit Dynamics Driving Fault Diagnosis Method for Bearing Variable Conditions[J]. China Mechanical Engineering, 2023, 34(08): 982-992.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2023.08.013

https://www.cmemo.org.cn/CN/Y2023/V34/I08/982

参考文献

［1］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.
CHEN Shiqian， PENG Zhike， ZHOU Peng， et al. Review of Signal Decomposition Theory and Its Applications in Machine Fault Diagnosis［J］. Journal of Mechanical Engineering， 2020， 56（17）：91-107.
［2］LEI Y， HE Z， ZI Y. A New Approach to Intelligent Fault Diagnosis of Rotating Machinery［J］. Expert Systems with Applications， 2008， 35（4）：1593-1600.
［3］SINGH V， VERMA N K. Intelligent Condition-based Monitoring Techniques for Bearing Fault Diagnosis［J］. IEEE Sensors Journal， 2020， 21（14）：15448-15457.
［4］李华，刘韬，伍星，等. 相关奇异值比的SVD在轴承故障诊断中的应用［J］. 机械工程学报， 2021， 57（21）：138-149.
LI Hua， LIU Tao， WU Xing， et al. Application of SVD Based on Correlated Singular Value Ratio in Bearing Fault Diagnosis［J］. Journal of Mechanical Engineering， 2021， 57（21）：138-149.
［5］GAO Y， YU D. Fault Diagnosis of Rolling Bearing Based on Laplacian Regularization［J］. Applied Soft Computing， 2021， 111：107651.
［6］LEI Z， WEN G， DONG S， et al. An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings under Polytropic Working Conditions［J］. IEEE Transactions on Instrumentation and Measurement， 2020， 70：1-14.
［7］ZHAO Z， WU S， QIAO B， et al. Enhanced Sparse Period-group Lasso for Bearing Fault Diagnosis［J］. IEEE Transactions on Industrial Electronics， 2018， 66（3）：2143-2153.
［8］侯钰哲，李舜酩，龚思琪. 滚动轴承故障特征选择的Filter与改进灰狼优化混合算法［J/OL］. 计算机集成制造系统［2022-04-12］. https：∥kns. cnki. net/kcms/detail/11. 5946. TP. 20220411. 1813. 036. html.
HOU Yuzhe， LI Shunming， GONG Siqi. Hybrid Algorithm of Filter and Improved Gray Wolf Optimization for Fault Feature Selection of Rolling Bearing［J/OL］. Computer Integrated Manufacturing Systems［2022-04-12］. https：∥kns. cnki. net/kcms/detail/11. 5946. TP. 20220411. 1813. 036. html.
［9］张龙，甄灿壮，易剑昱，等. 双通道特征融合CNN-GRU齿轮箱故障诊断［J］. 振动与冲击， 2021， 40（19）：239-245.
ZHANG Long， ZHEN Canzhuang， YI Jianyu， et al. Dual-hannel Feature Fusion CNN-GRU Gearbox Fault Diagnosis［J］. Journal of Vibration and Shock， 2021， 40（19）：239-245.
［10］APPANA D K， PROSVIRIN A， KIM J M. Reliable Fault Diagnosis of Bearings with Varying Rotational Speeds Using Envelope Spectrum and Convolution Neural Networks［J］. Soft Computing， 2018， 22（20）：6719-6729.
［11］HOU F， SELESNICK I， CHEN J， et al. Fault Diagnosis for Rolling Bearings under Unknown Time-varying Speed Conditions with Sparse Representation［J］. Journal of Sound and Vibration， 2021， 494：115854.
［12］张旭辉，张超，樊红卫，等. 快速谱峭度结合阶次分析滚动轴承故障诊断［J］. 振动. 测试与诊断， 2021， 41（6）：1090-1095.
ZHANG Xuhui， ZHANG Chao， FAN Hongwei， et al. Improved Fault Diagnosis of Rolling Bearing by Fast Kurtogram and Order Analysis［J］. Journal of Vibration， Measurement & Diagnosis， 2021， 41（6）：1090-1095.
［13］XU Y， CAI W， XIE T. Fault Diagnosis of Subway Traction Motor Bearing Based on Information Fusion under Variable Working Conditions［J］. Shock and Vibration， 2021， 2021：5522887.
［14］YANG X， CHI F， SHAO S， et al. Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer Learning［J］. Journal of Sensors， 2021， 2021：57144240.
［15］STRUC V， VESNICER B， MIHELIC F， et al. Removing Illumination Artifacts from Face Images Using the Nuisance Attribute Projection［C］∥2010 IEEE International Conference on Acoustics， Speech and Signal Processing. Dallas， 2010：846-849.
［16］VESNICER B， MIHELICˇ F. The Likelihood Ratio Decision Criterion for Nuisance Attribute Projection in GMM Speaker Verification［J］. EURASIP Journal on Advances in Signal Processing， 2008， 2008：1-11.
［17］SOLOMONOFF A， CAMPBELL W M， QUILLEN C. Nuisance Attribute Projection［J］. Speech Communication， 2007：7385151.
［18］JIANG H， CHEN J， DONG G. Hidden Markov Model and Nuisance Attribute Projection Based Bearing Performance Degradation Assessment［J］. Mechanical Systems and Signal Processing， 2016， 72：184-205.
［19］HUANG W， CHENG J， YANG Y. Rolling Bearing Fault Diagnosis and Performance Degradation Assessment under Variable Operation Conditions Based on Nuisance Attribute Projection［J］. Mechanical Systems and Signal Processing， 2019， 114：165-188.
［20］LYU Y， YANG D， YUAN R， et al. A Novel Multivariate Signal Processing-based Fault Diagnosis Approach of Rotating Machinery under Various Operating Conditions［J］. Measurement Science and Technology， 2022， 33（7）：075106.
［21］LIU X， HUANG H， XIANG J. A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine［J］. Sensors， 2020， 20（2）：420.
［22］SINGH S， HOWARD C Q， HANSEN C H， et al. Analytical Validation of an Explicit Finite Element Model of a Rolling Element Bearing with a Localised Line Spall［J］. Journal of Sound and Vibration， 2018， 416：94-110.
［23］JIANG H， CHEN J， DONG G， et al. An Intelligent Performance Degradation Assessment Method for Bearings［J］. Journal of Vibration and Control， 2017， 23（18）：3023-3040.
［24］LI R， LI S， XU K， et al. Adversarial Domain Adaptation of Asymmetric Mapping with CORAL Alignment for Intelligent Fault Diagnosis［J］. Measurement Science and Technology， 2022， 33（5）：055101.
［25］董绍江，朱朋，裴雪武，等. 基于子领域自适应的变工况下滚动轴承故障诊断［J］. 吉林大学学报（工学版）， 2022， 52（2）：288-295.
DONG Shaojiang， ZHU Peng， PEI Xuewu， et al. Fault Diagnosis of Rolling Bearing under Variable Operating Conditions Based on Subdomain Adaptation［J］. Journal of Jilin University（Engineering and Technology Edition）， 2022， 52（2）：288-295.
［26］HUANG H， BADDOUR N. Bearing Vibration Data Collected under Time-varying Rotational Speed Conditions［J］. Data in Brief， 2018， 21：1745-1749.

[1]	罗洁思, 于德介, 史美丽. 基于SVD和线调频小波路径追踪的转速波动齿轮箱故障诊断 [J]. J4, 201016, 21(16): 1947-1951.
[2]	胡超, 刘佐民. 基于材料匹配性的球轴承Heathcote黏-滑模型研究 [J]. J4, 201016, 21(16): 1969-1973.
[3]	仝兆景, 王鹏超, 樊永奎, 韩广洋, 王自奇. 基于改进精细复合多尺度样本熵与贝叶斯网络的滚动轴承故障诊断方法[J]. 中国机械工程, 2025, 36(12): 2952-2959.
[4]	张雄, 董乐聪, 王文强, 渠伟瀅, 万书亭. 融合二维卷积与门控循环神经网络的滚动轴承多故障诊断方法[J]. 中国机械工程, 2025, 36(12): 2978-2985.
[5]	杨慧明, 杨霞, 黄宇齐. 基于Paris模型的滚动轴承全周期疲劳寿命预测[J]. 中国机械工程, 2025, 36(09): 2003-2010.
[6]	冯思茜, 王家序, 张新, 黄欣玥. 基于共振解调新方法的滚动轴承故障诊断[J]. 中国机械工程, 2025, 36(09): 2022-2031.
[7]	陈剑, 严明辉, 陈品. 基于贝叶斯优化多尺度DenseNet的离心泵声信号故障诊断方法[J]. 中国机械工程, 2025, 36(09): 2032-2038.
[8]	张建宇, 王留震, 肖勇, 马雅楠. 滚动轴承的退化特征信息融合与剩余寿命预测[J]. 中国机械工程, 2025, 36(07): 1553-1561.
[9]	宋李俊, 刘松林, 辛玉, 马婧华, 谢正邱. 基于轴承退化状态评估和改进图注意力双向门控循环单元网络的轴承剩余寿命预测[J]. 中国机械工程, 2025, 36(07): 1562-1572.
[10]	杨康1, 陈学军1, 2, 张磊3, 刘烽3. 基于联合子域对比对齐的轴承跨域故障诊断[J]. 中国机械工程, 2025, 36(05): 1065-1073.
[11]	张申1, 梁嘉炜2, 吴动波3, 王辉4, 赵兵1, 许立君5, 周奋5. 航空发动机精锻叶片榫头加工夹具设计[J]. 中国机械工程, 2025, 36(04): 703-714.
[12]	焦华超, 孙文磊, 王宏伟. 基于类小波辅助分类生成对抗网络的轴承故障数据生成方法[J]. 中国机械工程, 2025, 36(03): 546-557.
[13]	张庆正1, 姜晨1, 高睿2, 蒋金鑫1. 砷化镓解理加工的划片过程仿真分析及工艺研究[J]. 中国机械工程, 2024, 35(12): 2203-2210.
[14]	李继猛, 王泽, 史清心, 孟宗. 基于图正则化约束频域组稀疏模型的风电机组滚动轴承故障诊断[J]. 中国机械工程, 2024, 35(11): 1909-1919.
[15]	郭海宇1, 邹圣公1, 张晓光2, 3, 4, 陆凡凡2, 陈洋2, 王涵2, 徐新志2. 基于多源小波变换神经网络的旋转机械轴承故障诊断[J]. 中国机械工程, 2024, 35(11): 2026-2034.