基于振动信号的机械故障特征提取与诊断研究
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摘要
摘要：机械故障诊断对于保障机械设备的安全、稳定运行具有重要意义。基于振动信号分析的机械故障诊断方法具有可在线、实时、非损伤、诊断便捷准确等优点,已经得到广泛应用。本文以轴承、齿轮为主要研究对象,针对机械故障特征提取与诊断问题,采用基于振动信号分析的方法,研究了基于经验模态分解的信号降噪方法；基于小波变换、小波包变换、Hilbert-Huang变换、独立分量分析等现代信号处理方法的机械故障特征提取技术,研究了基于支持向量机、最近邻分类器的机械故障识别方法。本文提出一些机械故障特征提取与诊断的新方法,主要研究内容包括以下几个方面：
(1)针对目前基于经验模态分解的振动信号降噪方法不能同时较好地处理高频内蕴模态函数与低频内蕴模态函数的噪声问题,研究提出一种改进的基于经验模态分解的降噪方法,结合现有的两种基于经验模态分解降噪方法的优点,分别对高频内蕴模态函数与低频内蕴模态函数采用不同的降噪方法。仿真和实验结果表明改进的基于经验模态分解的降噪方法具有更好地降噪性能。
(2)研究了基于相对小波能量与支持向量机的机械故障诊断方法。首先将机械故障振动信号进行离散小波分解,然后利用分解后各频带的相对小波能量作为特征向量,最后使用支持向量机作为分类器对机械故障进行分类。并以滚动轴承故障诊断为例验证了该方法能够较好地识别滚动轴承的故障类型及故障程度,具有一定的工程应用价值。
(3)针对机械设备在出现故障时其动力学特性往往呈现出复杂性和非线性,近年来提出的样本熵是一种度量信号复杂性的方法,与分形维数、Kolmogorov熵、李雅普诺夫指数等非线性动力学参数相比,可以较少地依赖于时间序列的长度。基于此提出一种基于小波包变换与样本熵的机械故障诊断方法,利用小波包变换对机械振动信号进行分解,然后计算分解后得到的各个频带的样本熵值作为特征向量,最后使用支持向量机进行故障识别。通过结合小波包技术,可以得到机械故障在不同频带的特征信息,与直接利用原始信号样本熵分析相比可以更全面、更准确地刻画机械故障特征。机械故障诊断实验表明该方法取得较好地识别效果,是一种有效地机械故障诊断方法。
(4)针对机械故障振动信号的时频特征提取问题,研究Hilbert谱时频特征提取方法。Hilbert谱是对振动信号能量精确的时频表示,反映了机械故障振动信号的时间和频率的分布情况,为了提取机械故障信号Hilbert谱特征,引入奇异值分解方法,利用Hilbert谱奇异值作为机械故障特征参数。该方法利用了奇异值分解稳定性好,可以较好地刻画时频矩阵特征的优点。实测轴承振动信号故障诊断实验表明该方法得到较好地识别效果,具有一定的应用价值。
(5)研究了基于独立分量分析的机械故障特征提取方法。提出一种基于独立分量分析与相关系数的机械故障诊断方法,通过对不同工况的机械故障信号分别进行独立分量分析,获得各种工况信号的独立分量,这些独立分量中蕴含了该工况振动信号的一些内在特征；接着利用样本与不同工况信号提取的独立分量的相关系数绝对值的和作为该样本的特征；最后使用支持向量机作为分类器进行识别。齿轮和轴承故障诊断实验表明该方法可以准确提取机械故障特征,获得较高的识别率。
Machinery fault diagnosis is importance to the safe and stability of machinery. With features of on-line, real time, non-destructive detection, convenient, fast and accurate, machinery fault diagnosis based on vibration signal processing is widely used in practice. This dissertation focus on bearing and gear fault diagnosis and take an intensive study on the machinery fault extraction and diagnosis. The work includes study of the Empirical Mode Decomposition (EMD) based signal denoising method; study of the wavelet transform, wavelet packet, Hilbert-Huang transform, Independent Component Analysis based machinery fault feature extraction; and study of the SVM and Nearest Neighbour based fault recogntion. Some new machinery fault feature extraction and diagnosis methods are proposed in this dissertation. The main researches include:
(1) Due to the limitations of current EMD based de-noising method can't meet the deamand of both high frequency Intrinsic Mode Functions (IMFs) and low frequency IMFs. An improved EMD based de-noising method is proposed which hybrids the merits of EMD based threshold de-noising method and Savitzky-Golay filtering method. In the proposed method, the high frequency IMFs and the low frequency IMFs are using different de-noising methods. The method is tested on simulated data and real vibration signal and the performance of the improved method is better than the EMD based threshold method and the Savitzky-Golay filtering method used alone.
(2) Study on the applicaion of relative wavelet energy and Support Vector Machine in machinery fault diagnosis. The original fault vilbration signal is decomposed by discrete wavelet transform. Then the relative wavelet energy is served as the feature vector. In the classification, the support vector machine method is used to identify the different machinery faults. Experiments were conducted on roller bearing fault diagnosis and the experimental results indicate that the proposed approach could reliably identify the different fault categories and levels of fault severity.
(3) Due to the complexity and non-linearity of the machinery vibration signal refelct the occurrence of the fault. Recently the Sample Entropy (SampEn) is proposed and can quantify the complexity of a signal and has the advantage of being less dependent on time series length than Fractal dimension, Kolmogorov entropy and Lyapunov exponent. A machinery fault diagnosis method based on Wavelet Packet Transform (WPT) and SampEn is proposed in this dissertation. The original machinery vibration signal is decomposed by wavelet packet transform. The SampEn of the resultant wavelet packet coefficients are calculated and served as feature vector. In the classification, the support vector machine method is used to identify the different faults. By combined with the wavlet packet transform, the feature information of different frequency band can get. This is more comprehensive and accurate characterised the machinery fault. Machinery fault diagnosis experiments indicate that the method can get better recognition result.
(4) Due to the problem of time-frequency feature extraction from the machinery fault vibration signal, the feature extraction from Hilbert spectrum method is studied. The Hilbert spectrum offers a time-frequency distribution of machinery fault vibraion signal. A new fault feature extraction method based on Hilbert spectrum and singular value decomposition is proposed and applied to the bearing falut diagnosis. In order to feature extraction from the Hilbert spectrum, the Sigular Value Decompostion based method which used the singular value of the Hilbert spectrum as the feature parameter is introduced. The proposed method has the merits of the good stability with the Singular Value Decompostion and can better describe the time-frequency matrix feature. For real vibration signal fault diagnosis experiment indicate that the satisfied results can be acquired. The proposed mehod proved to be valuable for engineering application.
(5) Study the application of Independent Component Analysis (ICA) on machinery fault feature extraction. A new feature extration method based on ICA and correlation coefficient is proposed. The ICA is used for vibration signal of each fault category and the extracted independent components include the information of the fault. Then the sum of the absolute correlation coefficients of the sample and the extracted indepent components of each category are used as the feature vetor. Finally the support vector machine is used as the classification method for fault diagnosis. The proposed fault feature extraction method is applied to two tasks:gear feault diagnosis and roller bearing fault diagnosis tasks. The proposed mehod could extract the feature of the machinery fault and acquire satisfied recognition results.
(1)针对目前基于经验模态分解的振动信号降噪方法不能同时较好地处理高频内蕴模态函数与低频内蕴模态函数的噪声问题,研究提出一种改进的基于经验模态分解的降噪方法,结合现有的两种基于经验模态分解降噪方法的优点,分别对高频内蕴模态函数与低频内蕴模态函数采用不同的降噪方法。仿真和实验结果表明改进的基于经验模态分解的降噪方法具有更好地降噪性能。
(2)研究了基于相对小波能量与支持向量机的机械故障诊断方法。首先将机械故障振动信号进行离散小波分解,然后利用分解后各频带的相对小波能量作为特征向量,最后使用支持向量机作为分类器对机械故障进行分类。并以滚动轴承故障诊断为例验证了该方法能够较好地识别滚动轴承的故障类型及故障程度,具有一定的工程应用价值。
(3)针对机械设备在出现故障时其动力学特性往往呈现出复杂性和非线性,近年来提出的样本熵是一种度量信号复杂性的方法,与分形维数、Kolmogorov熵、李雅普诺夫指数等非线性动力学参数相比,可以较少地依赖于时间序列的长度。基于此提出一种基于小波包变换与样本熵的机械故障诊断方法,利用小波包变换对机械振动信号进行分解,然后计算分解后得到的各个频带的样本熵值作为特征向量,最后使用支持向量机进行故障识别。通过结合小波包技术,可以得到机械故障在不同频带的特征信息,与直接利用原始信号样本熵分析相比可以更全面、更准确地刻画机械故障特征。机械故障诊断实验表明该方法取得较好地识别效果,是一种有效地机械故障诊断方法。
(4)针对机械故障振动信号的时频特征提取问题,研究Hilbert谱时频特征提取方法。Hilbert谱是对振动信号能量精确的时频表示,反映了机械故障振动信号的时间和频率的分布情况,为了提取机械故障信号Hilbert谱特征,引入奇异值分解方法,利用Hilbert谱奇异值作为机械故障特征参数。该方法利用了奇异值分解稳定性好,可以较好地刻画时频矩阵特征的优点。实测轴承振动信号故障诊断实验表明该方法得到较好地识别效果,具有一定的应用价值。
(5)研究了基于独立分量分析的机械故障特征提取方法。提出一种基于独立分量分析与相关系数的机械故障诊断方法,通过对不同工况的机械故障信号分别进行独立分量分析,获得各种工况信号的独立分量,这些独立分量中蕴含了该工况振动信号的一些内在特征；接着利用样本与不同工况信号提取的独立分量的相关系数绝对值的和作为该样本的特征；最后使用支持向量机作为分类器进行识别。齿轮和轴承故障诊断实验表明该方法可以准确提取机械故障特征,获得较高的识别率。
Machinery fault diagnosis is importance to the safe and stability of machinery. With features of on-line, real time, non-destructive detection, convenient, fast and accurate, machinery fault diagnosis based on vibration signal processing is widely used in practice. This dissertation focus on bearing and gear fault diagnosis and take an intensive study on the machinery fault extraction and diagnosis. The work includes study of the Empirical Mode Decomposition (EMD) based signal denoising method; study of the wavelet transform, wavelet packet, Hilbert-Huang transform, Independent Component Analysis based machinery fault feature extraction; and study of the SVM and Nearest Neighbour based fault recogntion. Some new machinery fault feature extraction and diagnosis methods are proposed in this dissertation. The main researches include:
(1) Due to the limitations of current EMD based de-noising method can't meet the deamand of both high frequency Intrinsic Mode Functions (IMFs) and low frequency IMFs. An improved EMD based de-noising method is proposed which hybrids the merits of EMD based threshold de-noising method and Savitzky-Golay filtering method. In the proposed method, the high frequency IMFs and the low frequency IMFs are using different de-noising methods. The method is tested on simulated data and real vibration signal and the performance of the improved method is better than the EMD based threshold method and the Savitzky-Golay filtering method used alone.
(2) Study on the applicaion of relative wavelet energy and Support Vector Machine in machinery fault diagnosis. The original fault vilbration signal is decomposed by discrete wavelet transform. Then the relative wavelet energy is served as the feature vector. In the classification, the support vector machine method is used to identify the different machinery faults. Experiments were conducted on roller bearing fault diagnosis and the experimental results indicate that the proposed approach could reliably identify the different fault categories and levels of fault severity.
(3) Due to the complexity and non-linearity of the machinery vibration signal refelct the occurrence of the fault. Recently the Sample Entropy (SampEn) is proposed and can quantify the complexity of a signal and has the advantage of being less dependent on time series length than Fractal dimension, Kolmogorov entropy and Lyapunov exponent. A machinery fault diagnosis method based on Wavelet Packet Transform (WPT) and SampEn is proposed in this dissertation. The original machinery vibration signal is decomposed by wavelet packet transform. The SampEn of the resultant wavelet packet coefficients are calculated and served as feature vector. In the classification, the support vector machine method is used to identify the different faults. By combined with the wavlet packet transform, the feature information of different frequency band can get. This is more comprehensive and accurate characterised the machinery fault. Machinery fault diagnosis experiments indicate that the method can get better recognition result.
(4) Due to the problem of time-frequency feature extraction from the machinery fault vibration signal, the feature extraction from Hilbert spectrum method is studied. The Hilbert spectrum offers a time-frequency distribution of machinery fault vibraion signal. A new fault feature extraction method based on Hilbert spectrum and singular value decomposition is proposed and applied to the bearing falut diagnosis. In order to feature extraction from the Hilbert spectrum, the Sigular Value Decompostion based method which used the singular value of the Hilbert spectrum as the feature parameter is introduced. The proposed method has the merits of the good stability with the Singular Value Decompostion and can better describe the time-frequency matrix feature. For real vibration signal fault diagnosis experiment indicate that the satisfied results can be acquired. The proposed mehod proved to be valuable for engineering application.
(5) Study the application of Independent Component Analysis (ICA) on machinery fault feature extraction. A new feature extration method based on ICA and correlation coefficient is proposed. The ICA is used for vibration signal of each fault category and the extracted independent components include the information of the fault. Then the sum of the absolute correlation coefficients of the sample and the extracted indepent components of each category are used as the feature vetor. Finally the support vector machine is used as the classification method for fault diagnosis. The proposed fault feature extraction method is applied to two tasks:gear feault diagnosis and roller bearing fault diagnosis tasks. The proposed mehod could extract the feature of the machinery fault and acquire satisfied recognition results.
引文
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