基于声发射技术的滚动轴承故障诊断时频分析方法研究
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摘要
滚动轴承是旋转机械中应用最广泛、也是最易损坏的关键零部件之一,对其进行状态监测和故障诊断有着广泛的经济和社会效益。声发射(acoustic emissionAE)技术应用于滚动轴承的状态监测和故障诊断相对于目前普遍采用的振动法有着许多优越性。
本文借鉴基于振动信号的滚动轴承故障诊断时频分析的成功经验,将各种典型的时频分析方法引入滚动轴承的声发射故障诊断领域,开展基于声发射技术的滚动轴承故障诊断时频分析方法的研究。工作主要包括以下四个方面:(1)开发了声发射数据采集系统,与滚动轴承故障实验台和SWAES全波形声发射检测仪配套,形成了滚动轴承故障全波形声发射检测系统。进行了滚动轴承典型故障的声发射试验,并分析了滚动轴承故障引发的AE信号的特点及特征提取原理。(2)分别发展了滚动轴承故障AE信号的STFT(Gabor变换)分析法、WVD分析法、小波尺度谱(小波再分配尺度谱)分析法,研究表明这些方法均能有效提取AE信号的特征以及反映AE信号中所蕴藏的特征信息,其二维、三维时频谱能准确描述滚动轴承故障引发的声发射事件,直观地表征AE信号中各个脉冲的数目、强度、在时频面上的分布及频率组成等。(3)提出了滚动轴承故障AE信号的小波包特征提取分析法,解决了从噪声污染严重、数据量大以及频率范围宽的实测AE信号中提取特征信号困难的难题,并能实现滚动轴承故障位置的精密诊断。(4)构造了适于滚动轴承故障AE信号特征提取的小波函数,该小波函数比目前普遍采用的Daubechies小波有着更好的使用效果,提高了滚动轴承故障AE信号小波分析的有效性和准确性。这些研究工作不仅能提高滚动轴承早期故障预报、诊断的效率和精度,而且有益于促进滚动轴承AE信号波形分析技术的发展。
Rolling bearings are the key components of rotating machineries, and are also very easy to be damaged. So it is very important to carry on the works of condition monitoring and fault diagnosis of rolling bearings, which will contribute to the development of our society and economy. Comparing with vibration methods that are used most extensively at present in the field of condition monitoring and fault diagnosis of rolling bearings, acoustic emission (AE) techniques have much superiority when applying for fault diagnosis of rolling bearings.
Time-frequency analysis method can be used to process effectively non-stationary time-variation signal, and are applied extensively for fault diagnosis of rolling bearings based on vibration methods recently years. So the paper introduce time-frequency analysis method to the field of fault diagnosis of rolling bearings based on acoustic emission techniques, and carry on the research on time-frequency analysis method of fault diagnosis of rolling bearings based on acoustic emission techniques. The works include mainly four aspects. (1) Developing the acoustic emission data collecting system, together with the fault experimental platforms of rolling bearings and SWAES full-waveform acoustic emission detection instrument etc, the experimental system detecting faults of rolling bearings based on acoustic emission techniques is constituted. The acoustic emission experiments of typical faults of rolling bearings are carried on, and the characteristics of AE signals initiated by faults of rolling bearings and fault feature extraction principles are studied. (2) Three time-frequency methods of fault AE signals of rolling bearings are developed respectively: STFT (Gabor transform) analysis method, wavelet scalogram (wavelet reassigned scalogram) analysis method and WVD analysis method. The two-dimension and three-dimension time-frequency spectrums of all the methods can correctly describe AE events initiated by failures of rolling bearings, and can represent audio-visually the number, strength, frequency compositions and distribution in time-frequency surface of the pulse signals. So the effective information required by the precise diagnosis of rolling bearings can be obtained. (3) Actual measurement AE signals have three main shortcomings: containing many noise signals, having very big date size and very broad frequency scope. So it very difficult to extract feature signals from actual measurement AE signals. However, the feature extraction analysis method of wavelet packet is put forward by the paper, ant the method can solve the problems effectively. (4) A wavelet function is designed, which can be used effectively to extract fault features of AE signals of rolling bearings. The function is more effective than Daubechies wavelet function that is used widely at present, and can improve greatly the availability and accuracy of wavelet analysis of fault AE signals of rolling bearings. These research findings not only can improve greatly the efficiencies and accuracies of incipient faults prediction and diagnosis of rolling bearings, but also are favorable to promote the waveforms analysis techniques of AE signals of rolling bearings.
本文借鉴基于振动信号的滚动轴承故障诊断时频分析的成功经验,将各种典型的时频分析方法引入滚动轴承的声发射故障诊断领域,开展基于声发射技术的滚动轴承故障诊断时频分析方法的研究。工作主要包括以下四个方面:(1)开发了声发射数据采集系统,与滚动轴承故障实验台和SWAES全波形声发射检测仪配套,形成了滚动轴承故障全波形声发射检测系统。进行了滚动轴承典型故障的声发射试验,并分析了滚动轴承故障引发的AE信号的特点及特征提取原理。(2)分别发展了滚动轴承故障AE信号的STFT(Gabor变换)分析法、WVD分析法、小波尺度谱(小波再分配尺度谱)分析法,研究表明这些方法均能有效提取AE信号的特征以及反映AE信号中所蕴藏的特征信息,其二维、三维时频谱能准确描述滚动轴承故障引发的声发射事件,直观地表征AE信号中各个脉冲的数目、强度、在时频面上的分布及频率组成等。(3)提出了滚动轴承故障AE信号的小波包特征提取分析法,解决了从噪声污染严重、数据量大以及频率范围宽的实测AE信号中提取特征信号困难的难题,并能实现滚动轴承故障位置的精密诊断。(4)构造了适于滚动轴承故障AE信号特征提取的小波函数,该小波函数比目前普遍采用的Daubechies小波有着更好的使用效果,提高了滚动轴承故障AE信号小波分析的有效性和准确性。这些研究工作不仅能提高滚动轴承早期故障预报、诊断的效率和精度,而且有益于促进滚动轴承AE信号波形分析技术的发展。
Rolling bearings are the key components of rotating machineries, and are also very easy to be damaged. So it is very important to carry on the works of condition monitoring and fault diagnosis of rolling bearings, which will contribute to the development of our society and economy. Comparing with vibration methods that are used most extensively at present in the field of condition monitoring and fault diagnosis of rolling bearings, acoustic emission (AE) techniques have much superiority when applying for fault diagnosis of rolling bearings.
Time-frequency analysis method can be used to process effectively non-stationary time-variation signal, and are applied extensively for fault diagnosis of rolling bearings based on vibration methods recently years. So the paper introduce time-frequency analysis method to the field of fault diagnosis of rolling bearings based on acoustic emission techniques, and carry on the research on time-frequency analysis method of fault diagnosis of rolling bearings based on acoustic emission techniques. The works include mainly four aspects. (1) Developing the acoustic emission data collecting system, together with the fault experimental platforms of rolling bearings and SWAES full-waveform acoustic emission detection instrument etc, the experimental system detecting faults of rolling bearings based on acoustic emission techniques is constituted. The acoustic emission experiments of typical faults of rolling bearings are carried on, and the characteristics of AE signals initiated by faults of rolling bearings and fault feature extraction principles are studied. (2) Three time-frequency methods of fault AE signals of rolling bearings are developed respectively: STFT (Gabor transform) analysis method, wavelet scalogram (wavelet reassigned scalogram) analysis method and WVD analysis method. The two-dimension and three-dimension time-frequency spectrums of all the methods can correctly describe AE events initiated by failures of rolling bearings, and can represent audio-visually the number, strength, frequency compositions and distribution in time-frequency surface of the pulse signals. So the effective information required by the precise diagnosis of rolling bearings can be obtained. (3) Actual measurement AE signals have three main shortcomings: containing many noise signals, having very big date size and very broad frequency scope. So it very difficult to extract feature signals from actual measurement AE signals. However, the feature extraction analysis method of wavelet packet is put forward by the paper, ant the method can solve the problems effectively. (4) A wavelet function is designed, which can be used effectively to extract fault features of AE signals of rolling bearings. The function is more effective than Daubechies wavelet function that is used widely at present, and can improve greatly the availability and accuracy of wavelet analysis of fault AE signals of rolling bearings. These research findings not only can improve greatly the efficiencies and accuracies of incipient faults prediction and diagnosis of rolling bearings, but also are favorable to promote the waveforms analysis techniques of AE signals of rolling bearings.
引文
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