| 朱江艳,马 军,杨创艳,李 祥,刘桂敏.基于 FastICA-BAS-MCKD 的滚动轴承
复合故障特征提取方法[J].电子测量与仪器学报,2021,35(8):107-117 |
| 基于 FastICA-BAS-MCKD 的滚动轴承
复合故障特征提取方法 |
| Compound fault feature extraction method of rolling bearingbased on FastICA-BAS-MCKD |
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| DOI： |
| 中文关键词: 复合故障 盲源分离 天牛须算法 最大相关峭度解卷积 |
| 英文关键词:compound fault blind source separation beetle antennae search algorithm maximum correlated kurtosis deconvolution |
| 基金项目:国家自然科学基金（51765002,61663017）、云南省科技厅科技计划项目（2019FD042）资助 |
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| Author | Institution |
| Zhu Jiangyan | 1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,2. Key Laboratory of Artificial Intelligence of Yunnan Province |
| Ma Jun | 1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,2. Key Laboratory of Artificial Intelligence of Yunnan Province，3. Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province |
| Yang Chuangyan | 1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,2. Key Laboratory of Artificial Intelligence of Yunnan Province |
| Li Xiang | 1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,2. Key Laboratory of Artificial Intelligence of Yunnan Province |
| Liu Guimin | 1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,2. Key Laboratory of Artificial Intelligence of Yunnan Province |
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| 中文摘要: |
| 针对强背景噪声下轴承复合故障特征难以分离提取的问题,提出了一种基于快速独立成分分析-天牛须-最大相关峭度
解卷积算法(FastICA-BAS-MCKD)的滚动轴承复合故障特征提取方法。 首先,引入 FastICA 对滚动轴承多通道故障信号进行盲
源分离;其次,利用 BAS 算法同步优化 MCKD 算法的解卷积周期 T、滤波器长度 L 和移位数 M,构建基于 BAS-MCKD 的滚动轴
承振动信号自适应分析方法;然后,应用 BAS-MCKD 方法处理分离后的信号,实现分离信号的降噪和特征增强;最后,应用希尔
伯特解调方法对 MCKD 处理后的信号进行包络谱分析,实现滚动轴承不同类型故障的识别。 仿真和实测信号的分析结果表
明,所提方法能清晰地从复合故障信号中提取出单一故障特征频率,为滚动轴承复合故障特征提取提供了一种有效的解决
方案。 |
| 英文摘要: |
| Aiming at the problem that bearing composite fault features are difficult to be separated and extracted under strong background
noise, a new compound faults diagnosis method is proposed in this paper based on fast independent component analysis ( FastICA),
beetle antennae search algorithm ( BAS) and maximum correlated kurtosis deconvolution ( MCKD). Firstly, FastICA method is
introduced for blind separation of rolling bearing multi-channel fault signals. Secondly, the deconvolution period T, filter length L and
shift number M of the deconvolution algorithm for MCKD are simultaneously optimized by using BAS. Then an adaptive analysis method
based on BAS-MCKD for vibration signal of rolling bearing is constructed to achieve noise reduction and feature enhancement of separated
signals. Finally, the Hilbert demodulation method is used to analyze the envelope spectrum of the signal processed by MCKD to realize
the identification of different types of rolling bearing faults. The analysis results of simulation and measured signals show that the
proposed method can clearly extract the single fault characteristic frequency from the composite fault signal, which provides an effective
solution for the complex fault characteristic extraction of rolling bearing. |
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