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Alternative TitleFault Detection of Rolling Bearing based on Processing of Vibration Signal
Thesis Advisor常红星
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword滚动轴承 小波分析 振动信号 故障诊断 特征提取 数据采集系统 Rolling Bearings Wavelet Analysis Vibration Signal Feature Extraction Fault Diagnosis Data Acquisition System
Abstract滚动轴承是旋转机械中应用最为广泛的机械零件,也是最易损坏的元件之一。目前大部分企业对轴承进行质量检测时都是凭借检验员的感觉判断,个人主观因素难以消除,检测结果不是很理想。针对这些问题,本文展开了基于振动信号处理的滚动轴承故障检测方法研究,具体工作如下: 首先,从理论上分析总结了滚动轴承结构的振动机理,失效形式,振动类型以及发生故障的原因,并给出了滚动轴承故障特征频率的计算公式,为滚动轴承振动故障诊断提供了理论依据。 其次,研究了小波包络谱分析法的基本理论,应用小波阈值降噪方法对轴承振动信号进行小波降噪处理,为轴承故障信号的分析创造了条件。再对降噪后的振动信号进行小波分解,重构,以及希尔波特变换,提取故障轴承的特征频率,与计算出来的理论频率相比,来确定轴承的故障类型。 最后,经过对大量实验数据的处理和分析,诊断结果令人满意,证明了实验方法的正确性,同时也说明小波分析确实为滚动轴承故障诊断提供了强有力的分析手段。并在此基础上设计了基于单片机的加速度传感器数据采集系统。
Other AbstractRolling bearings are the most conventional components in vast majority of rotating machines, and they are also easily to be damaged. Nowadays, in most companies the quality inspection of the rolling bearings are mostly based on the personal sense of QA Test Technician, therefore, it is very hard to eliminate individual subjective factors, and the result will not be very good. To solve these problems, in this paper we research the methods of the fault detection of rolling bearings based on vibration signal processing. The specific work are maily as follows: Firstly, the vibration mechanism of rolling bearing structure, the failure forms, vibration types and source of trouble are analyzed. The formula of characteristic frequency of fault bearing is given. It provides theoretical foundation for the bearing fault diagnosis. Secondly, the application of the envelope spectrum of wavelet analysis is studied. Threshold de-noising method in wavelet domain is used for bearing vibration signal denoising, which forms the foundation for the analysis of the bearing fault signal. By the methods of wavelet decomposition, reconstruction and hilbert transform, the characteristic frequency of fault bearings is picked up, and it would be compared with the theoretical frequencies. Then, which kind of faults of the rolling bearings can be determined Finally, a large number of experimental data are analyzed, and the result is satisfied. It can be proved that the method used in this paper is correct and it also means that the wavelet analysis theory is one of the most useful methods for faults diagnosis of rolling bearings. An acceleration sensor data acquisition system is designed here based on single-chip microcomputer.
Other Identifier200828014628038
Document Type学位论文
Recommended Citation
GB/T 7714
蒋志同. 基于振动信号处理的滚动轴承故障检测方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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