第二代小波变换在旋转机械故障诊断中的应用研究
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摘要
小波分析在时域和频域同时具有良好的局部化特性,因此较Fourier分析具有更大的优越性。目前在旋转机械故障诊断中得到了广泛应用。第2代小波变换继承了传统小波变换的时频局部化特性,所有的运算在时域上进行,小波基函数不再是由某一个函数的平移和伸缩而产生,具有算法结构简单、速度高、占用内存少等优点。本文利用第2代小波变换,针对旋转机械转子和轴承故障诊断问题,进行了如下研究工作:
(1)基于第2代小波变换的转子故障信号降噪处理。传统的小波降噪方法对转子信号进行去噪处理时,转子转速和信号采样频率对小波分解层数的选择具有很大的影响,因此,去噪过程难于自动完成。本文针对该问题,利用第2代小波变换,提出了基于尺度变换的小波自动去噪方法,通过对原始信号按一定方法进行重采样,并与小波软阈值去噪方法相结合,从而实现去噪处理,该方法可以消除转速和采样频率的影响,毋须人为选取小波分解层数,整个去噪过程自动完成。最后,利用转子故障模拟实验台和航空发动机转子实验器采集不平衡、不对中、油膜涡动及碰摩故障数据进行了降噪分析,取得了满意的效果。
(2)基于第2代小波变换的转子故障信号特征提取。针对小波频段能量特征提取受旋转速度和采样频率的变化影响,本文运用尺度变换原理,通过对原始时间信号进行重采样,对重采样后的信号进行第2代小波变换,并统一分解到给定层上,从而获取信号的频带特征。该方法能够消除转子转速和信号采样频率对小波分解频带分布的影响,所提取出的频带能量特征具有统一的物理意义。最后,利用转子故障模拟实验台和航空发动机转子实验器,获取了转子不平衡、不对中、油膜涡动及碰摩4类故障数据,并进行了特征提取,并构造了结构自适应神经网络进行了诊断分析。获得了很高的识别率。
(3)基于第2代小波变换的滚动轴承故障信号分析。分析了滚动轴承故障特征,利用第2代小波变换将滚动轴承故障振动加速度信号分解到不同尺度,提取出共振频带,然后利用Hirbert变换进行解调,再对解调后的信号进行频谱分析得到小波包络谱,从包络谱上获取轴承故障特征信息。本文利用美国Case Western Reserve University电气工程实验室的滚动轴承故障模拟实验台获取的滚动轴承故障数据进行了分析验证,结果表明了方法的有效性。
Wavelet analysis all has fine localization characteristic property in the time domain and frequency region, therefore it is better than Fourier analysis.At present, wavelet transform is applied widely in rotating machine fault diagnosis. The 2nd generation wavelet transform inherits the advantages of tradition wavelet transform, and its computation is carried out on the time domain, the window functions are not generated by the dilation or compression of a mother wavelet no longer. In addition, the 2nd generation wavelet transform has these merits such as simple algorithm structure, less computation, less memory. In this paper, the 2nd generation wavelet is used to carry out the fault diagnosis of the rotor and ballbearing.
(1) Denoising the rotor fault signals based on the 2nd generation wavelet transform has been studied. When the traditional wavelet denoising methods are used to denosise and process the rotor signals, the rotating rate and the signal sampling frequency have very great effect on the choosing of the number of the wavelet decomposition layers.So, it is difficult to achieve the process of denoising automatically. In this paper, aiming at the above problem, the 2nd generation wavelet are full used, and a wavelet automatical denoising method based on scale transform is advanced, and the original signals are sampling repeatedly according to the certain method. Meanwhile, the wavelet soft threshold denosising method are used together to achieve the denoising. This method can eliminate the negative effects of the rotating speed and the sampling frequency, and the number of the wavelet decomposition layers can be automatically choosed. In the whole process of denoising, all steps can be accomplished automatically. Finally, the fault datas gathered from the rotor test-bed and the aero-engine rotor experimental rig on imbalance, misalignment, oil-film whorl and rubbing conditions are used to do the denoising analysis, and the approving results are obtained.
(2) The feature extraction of rotor fault signals based on the 2nd generation wavelet transform is studied. Aiming at the problem that the wavelet frequency band energy characteristics are affected by the changes of the rotating speed and the sampling frequency, in this paper, according to the scale transform theories, the original signals are sampling repeatedly, and the 2nd generation wavelet transform is used for the signals sampled. Afterward, the obtained signals are decomposed to fixed layer so as to obtain the frequency band characteristics of the original signals. This method can eliminate the negative effects of the rotating speed and the sampling frequency, and the frequency band energy characteristics extracted have the unitive physical significance. Finally, the the rotor test-bed and the aero-engine rotor experimental rig are used to obtain 4 kinds fault data samples on imbalance, misalignment, oil-film whorl and rubbing conditions of rotor system, and the feature extraction has been done. Afterward, the structure self-adaptation neural network is constructed for the diagnosis and analysis of the fault data samples, and the very high distinguishing rate is obtained.
(3) the ball bearing fault signal analysis based on the 2nd generation wavelet transform is studied. Firstly, the ball bearing fault characteristics are analyzed, and the 2nd generation wavelet transform is used to decompose the vibration acceleration signals of ball bearing fualts to different scales, and the resonance frequency band is extracted. Afterward, the Hirbert transform is used to demodulate the signals, and the frequency analysis of the signals demodulated has been done to obtain the wavelet spectra from which the fault characteristic informations of ball bearings are obtained. Finally, the analysis and validation have been done by using the ball bearing fault data which have been gathered form the ball bearing fault test-bed belong to the electric engineering laboratory of Case Western Reserve University of America. The results show that the method is very effective.
(1)基于第2代小波变换的转子故障信号降噪处理。传统的小波降噪方法对转子信号进行去噪处理时,转子转速和信号采样频率对小波分解层数的选择具有很大的影响,因此,去噪过程难于自动完成。本文针对该问题,利用第2代小波变换,提出了基于尺度变换的小波自动去噪方法,通过对原始信号按一定方法进行重采样,并与小波软阈值去噪方法相结合,从而实现去噪处理,该方法可以消除转速和采样频率的影响,毋须人为选取小波分解层数,整个去噪过程自动完成。最后,利用转子故障模拟实验台和航空发动机转子实验器采集不平衡、不对中、油膜涡动及碰摩故障数据进行了降噪分析,取得了满意的效果。
(2)基于第2代小波变换的转子故障信号特征提取。针对小波频段能量特征提取受旋转速度和采样频率的变化影响,本文运用尺度变换原理,通过对原始时间信号进行重采样,对重采样后的信号进行第2代小波变换,并统一分解到给定层上,从而获取信号的频带特征。该方法能够消除转子转速和信号采样频率对小波分解频带分布的影响,所提取出的频带能量特征具有统一的物理意义。最后,利用转子故障模拟实验台和航空发动机转子实验器,获取了转子不平衡、不对中、油膜涡动及碰摩4类故障数据,并进行了特征提取,并构造了结构自适应神经网络进行了诊断分析。获得了很高的识别率。
(3)基于第2代小波变换的滚动轴承故障信号分析。分析了滚动轴承故障特征,利用第2代小波变换将滚动轴承故障振动加速度信号分解到不同尺度,提取出共振频带,然后利用Hirbert变换进行解调,再对解调后的信号进行频谱分析得到小波包络谱,从包络谱上获取轴承故障特征信息。本文利用美国Case Western Reserve University电气工程实验室的滚动轴承故障模拟实验台获取的滚动轴承故障数据进行了分析验证,结果表明了方法的有效性。
Wavelet analysis all has fine localization characteristic property in the time domain and frequency region, therefore it is better than Fourier analysis.At present, wavelet transform is applied widely in rotating machine fault diagnosis. The 2nd generation wavelet transform inherits the advantages of tradition wavelet transform, and its computation is carried out on the time domain, the window functions are not generated by the dilation or compression of a mother wavelet no longer. In addition, the 2nd generation wavelet transform has these merits such as simple algorithm structure, less computation, less memory. In this paper, the 2nd generation wavelet is used to carry out the fault diagnosis of the rotor and ballbearing.
(1) Denoising the rotor fault signals based on the 2nd generation wavelet transform has been studied. When the traditional wavelet denoising methods are used to denosise and process the rotor signals, the rotating rate and the signal sampling frequency have very great effect on the choosing of the number of the wavelet decomposition layers.So, it is difficult to achieve the process of denoising automatically. In this paper, aiming at the above problem, the 2nd generation wavelet are full used, and a wavelet automatical denoising method based on scale transform is advanced, and the original signals are sampling repeatedly according to the certain method. Meanwhile, the wavelet soft threshold denosising method are used together to achieve the denoising. This method can eliminate the negative effects of the rotating speed and the sampling frequency, and the number of the wavelet decomposition layers can be automatically choosed. In the whole process of denoising, all steps can be accomplished automatically. Finally, the fault datas gathered from the rotor test-bed and the aero-engine rotor experimental rig on imbalance, misalignment, oil-film whorl and rubbing conditions are used to do the denoising analysis, and the approving results are obtained.
(2) The feature extraction of rotor fault signals based on the 2nd generation wavelet transform is studied. Aiming at the problem that the wavelet frequency band energy characteristics are affected by the changes of the rotating speed and the sampling frequency, in this paper, according to the scale transform theories, the original signals are sampling repeatedly, and the 2nd generation wavelet transform is used for the signals sampled. Afterward, the obtained signals are decomposed to fixed layer so as to obtain the frequency band characteristics of the original signals. This method can eliminate the negative effects of the rotating speed and the sampling frequency, and the frequency band energy characteristics extracted have the unitive physical significance. Finally, the the rotor test-bed and the aero-engine rotor experimental rig are used to obtain 4 kinds fault data samples on imbalance, misalignment, oil-film whorl and rubbing conditions of rotor system, and the feature extraction has been done. Afterward, the structure self-adaptation neural network is constructed for the diagnosis and analysis of the fault data samples, and the very high distinguishing rate is obtained.
(3) the ball bearing fault signal analysis based on the 2nd generation wavelet transform is studied. Firstly, the ball bearing fault characteristics are analyzed, and the 2nd generation wavelet transform is used to decompose the vibration acceleration signals of ball bearing fualts to different scales, and the resonance frequency band is extracted. Afterward, the Hirbert transform is used to demodulate the signals, and the frequency analysis of the signals demodulated has been done to obtain the wavelet spectra from which the fault characteristic informations of ball bearings are obtained. Finally, the analysis and validation have been done by using the ball bearing fault data which have been gathered form the ball bearing fault test-bed belong to the electric engineering laboratory of Case Western Reserve University of America. The results show that the method is very effective.
引文
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