| 张 龙,赵丽娟,杨锦雯,涂文兵,张 号.动力学小波字典驱动的轴承故障个性化稀疏诊断[J].电子测量与仪器学报,2022,36(7):213-222 |
| 动力学小波字典驱动的轴承故障个性化稀疏诊断 |
| Dynamic wavelet dictionary driven bearing fault personalization sparse diagnosis |
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| DOI： |
| 中文关键词: 稀疏分解 有限元技术 动力学小波 个性化诊断 特征提取 |
| 英文关键词:sparse decomposition FEM technology dynamical wavelet personalized diagnosis feature extraction |
| 基金项目:江西省自然科学基金项目（20212BAB204007）、江西省研究生创新资金项目（YC2020 S335，YC2021 S422）资助 |
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| Author | Institution |
| Zhang Long | 1. School of Mechatronics&Vehicle Engineering, East China Jiaotong University,2. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure |
| Zhao Lijuan | 1. School of Mechatronics&Vehicle Engineering, East China Jiaotong University,2. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure |
| Yang Jinwen | 1. School of Mechatronics&Vehicle Engineering, East China Jiaotong University,2. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure |
| Tu Wenbing | 1. School of Mechatronics&Vehicle Engineering, East China Jiaotong University,2. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure |
| Zhang Hao | 1. School of Mechatronics&Vehicle Engineering, East China Jiaotong University,2. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure |
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| 中文摘要: |
| 针对轴承信号稀疏分解方法中因轴承个性化振动行为导致稀疏分解字典与故障信号适配性差,以及因字典参数设置、
选取不当而使其在实际应用中稀疏分解效果不佳的问题,提出一种基于动力学小波字典的个性化稀疏诊断方法。 该方法基于
有限元技术和稀疏分解的思想,根据轴承所处运行工况的不同,建立个性化动力学仿真模型,仿真出振动信号,并从中提取出单
个瞬态冲击作为字典原子,将原子进行拓普利兹(Toeplitz)延拓生成动力学小波分析字典,结合正交匹配追踪算法(OMP)对信
号进行稀疏分解并重构,提取轴承故障特征频率。 动力学模型仿真信号和试验台信号的分析结果表明,相比常用的相关滤波算
法(CFA)构造的参数字典、K-SVD 自学习字典和快速谱峭度方法,所提出的方法可以更加准确有效地提取故障特征成分,且具
有较好的的稳定性和可拓展性。 |
| 英文摘要: |
| Sparse decomposition method usually shows poor performance in terms of matching with the fault signal due to the personalized
vibration behavior of the bearing, and has some drawbacks especially in practical applications due to the improper setting and selection of
dictionary parameters. To address these issues, a novel personalized sparse diagnosis method based on dynamical wavelet dictionary was
presented. It lies in the foundation for the idea of finite element model (FEM) technology and sparse decomposition. In order to obtain
dictionary atoms, according to the different operating conditions, the FEM is built to generate the vibration signals which accord with the
bearings features of faults, and the fault transient shock extracting from vibration signal will be regarded as dictionary atom. The
dynamical wavelet analysis dictionary can be constructed via atomic Toplitz transformation. The bearing fault feature frequencies can be
extracted by performing sparse decomposition and reconstruction of the signal with the help of orthogonal matching pursuit (OMP). The
FEM simulation signal and experiment signal results show that the presented scheme can extract the fault features more effectively than
the popular parametric dictionary based on a correlation filtering algorithm ( CFA), fast-kurtogram and the K-SVD self-learning
dictionary and has a stability and scalability. |
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