基于非高斯、非平稳信号处理的机械故障特征提取方法研究
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摘要
机械故障诊断是以机械学为基础的一门综合技术。机械故障诊断的关键是如何从机械故障振动信号中提取故障特征,信号分析和处理是特征提取最常用的方法。机械故障振动信号本质上是非高斯、非平稳信号,近年来,为满足对机器故障进行早期检测、诊断的需要,非高斯、非平稳信号处理方法在机械故障诊断领域受到了广泛的关注。如何确实有效地结合振动信号自身特点,创新性的应用非高斯、非平稳信号处理理论解决机械故障诊断中的信号降噪、故障特征提取等问题是当前机械故障诊断领域迫切需要研究的重点课题之一。本论文正是基于以上要求而开展研究工作的,重点研究适合于机械故障特征提取的非高斯、非平稳信号处理方法,主要完成了以下几方面的研究工作:
1)研究了基于STFT的振动信号解调及频谱细化分析方法及其应用。首次对基于STFT的振动信号解调方法的原理和影响其解调性能的各种因素进行了严格的理论分析,指出该解调方法实质是基于复解析带通滤波的Hilbert变换解调法。首次从数学上严格证明了在利用Hilbert变换进行包络解调分析时,只要带通滤波器通带范围包括调制信号的部分频率成分,就可解调出被调制信号的周期成分。基于以上理论分析,给出了实用的基于STFT的自适应振动信号解调新算法。针对复杂噪声环境下微弱周期性故障信号特征的检测问题,提出了奇异值分解降噪和STFT解调相结合的检测新方法。此外,将STFT引入到信号频谱细化分析,提出了基于STFT的无需频率成份调整的信号频谱细化分析新方法。
2)研究了基于滤波器组理论的振动信号处理方法。通过分析小波与滤波器组的关系,指出机械故障诊断领域所应用的小波(包)分解实质是利用共轭镜像对称滤波器组(CQMF)对信号进行分解。针对目前小波(包)分解方法用于振动信号分析时存在的不足,首次提出了基于正交镜像对称滤波器组(QMF)的振动信号分解方法,并构造了一种具有线性相位的两通道QMF滤波器组。与同阶的小波滤波器相比,该QMF滤波器不仅滤波性能更优,而且其滤波系数的求取更加便捷。为了解决常规两通道滤波器组分解算法中存在的子带信号组频带错位问题,引入了无频带错位的QMF滤波器组分解算法,基于此分解算法,提出了用于早期故障自动检测的振动信号解调新方法和自适应频谱细化方法。鉴于信号两通道塔形分解在实际振动信号分析中存在的不足,首次提出了信号三通道塔形分解方案作为其补充,并给出了相应的分解算法。此外,推广了现有的平稳小波包分解算法,首次提出了基于QMF滤波器组的平稳滤波器组分解算法,仿真和实测振动信号分析结果表明,该算法与平稳小波包分解算法相比具有更优的滤波性能。
3)研究了基于连续小波滤波器的微弱冲击信号特征提取方法。理论分析和仿真分析结果表明,信息工程领域中常用系列连续小波基及其常规的时间—尺度分析方法不适合微弱冲击信号的特征提取。基于适合微弱冲击信号特征提取的连续小波滤波器的统一形式,构造了一种易实现小波频谱中心频率和频窗宽度调整的频域紧支小波滤波器。仿真信号分析结果表明,当该小波滤波器参数选择合理时,可以有效地增强微弱冲击信号的冲击特征。关于如何快速地设计出适合微弱冲击信号特征提取的最优频域紧支小波滤波器问题,提出了以峭度系数为优化目标利用遗传算法进行寻优的自适应设计方法。研究了自适应小波预处理方法在弱冲击调制类二阶循环平稳信号解调中的应用,首次提出了基于最优频域紧支小波滤波器预处理的谱相关密度解调分析新方法。仿真和实测振动信号的分析结果均表明,该方法不仅可以有效地解调出微弱周期性故障冲击信号的故障特征频率,而且大大削减了原始常规方法的计算量,提高了二阶循环平稳信号解调方法的实用性。
4)研究了振动信号的EMD处理方法。提出了基于波形相似度比较的端点极值延拓新方法用于解决EMD分解过程中存在的端点效应问题。仿真信号和实测转子失衡故障振动信号分析结果表明,对于规则信号的EMD分解,利用该方法进行端点延拓,可以有效地避免端点处包络误差对分解结果产生的不利影响,得到准确的IMF分量。针对EMD分解过程中可能存在的模态混叠现象,分析了产生模态混叠现象的原因,指出原始信号中存在的一定能量大小的各种非规则的平稳和非平稳噪声是产生模态混叠的根源,并基于此结论和对EMD分解方法本质的认识,提出了基于自适应滤波的模态混叠消除新方法。仿真信号和实测齿轮故障振动信号分析结果均表明,该方法可以比较有效地消除模态混叠现象。
5)研究了振动信号的盲处理方法及其应用。通过对多通道MBD频域实现方法的剖析,提出了一种适合振动信号特征提取要求的多通道振动信号盲处理新方法—平稳滤波器组分解滤波和ICA算法相结合的振动信号盲处理方法,并将该方法应用于强噪声环境下机车柴油机增压器转频振动信号特征的提取工作,取得了令人十分满意的效果。针对目前常用单通道振动信号盲解卷算法—最小熵解卷(MED)算法在应用中存在的不足,提出了一种新的单通道振动信号盲解卷算法—基于ICA的盲解卷方法。仿真信号和实测振动信号分析的结果均表明,该方法与最小熵解卷(MED)算法相比不仅收敛速度快、鲁棒性强,而且在提取强噪声环境下微弱冲击信号特征方面,特征提取效果更明显。
Mechanical fault diagnostics is an integrated technical subject based on mechanics. The key of mechanical fault diagnosis is how to extract the fault feature from mechanical fault vibration signals which are non-gaussian and non-stationary signals. Recently, in order to meet the need of early detection and diagnosis on mechanical faults, non-gaussian and non-stationary signal processing methods attract extensive attention in the area of mechanical fault diagnosis. How to creatively apply non-gaussian and non-stationary signal processing theory to solve the problems about signal denoising and feature extraction is one of the important research issues in the area of mechanical fault diagnosis. Based on the above request, this dissertation does some researches on non-gaussian and non-stationary signal processing methods suitable for mechanical fault feature extraction. The main research work by the authors is introduced briefly as follows:
Firstly, vibration signal demodulation analysis and frequency spectrum Zoom-FFT technologies based on STFT are studied. From the point of view of signal filtering, the principle of vibration signal demodulation method based on STFT is described and some factors affecting the demodulating performance are discussed. Through theoretical analysis, it is pointed out that the vibration signal demodulation method based on STFT is a special demodulation method based on complex analytic band-pass filtering and Hilbert transform. It is theoretically proven that the signal, which has the same period with the modulated signal, can be restored by demodulation analysis if some frequency elements of the modulated signal are located in pass range of a complex analytic band-pass filter. Based on the above analysis, the novel practical STFT based adaptive vibration signal demodulation algorithm is proposed. About the problem of weak periodic fault feature extraction in complicated background noise environment, a new detection method based on the combination of singular value decomposition denoising and STFT demodulation technique is put forward. In addition, an improved STFT based frequency spectrum Zoom-FFT technology is proposed.
Secondly, vibration signal processing technologies based on filterbank theory are studied. For overcoming the deficiency of current discrete wavelet and wavelet packet transform based vibration signal decomposition method, the decomposition method based on quadrature mirror filterbank (QMF) is first proposed and a two channel QMF with linear phase is constructed. Compared with the wavelet filters, constructed QMF filters with the same filter order as the wavelet filters have better filtering performance and the filter coefficients can be obtained more easily. In order to solve the problem of the frequency band derangement in the normal two channel filter band decomposition algorithm, an improved QMF decomposition algorithm is proposed. Based on the improved algorithm, a new vibration signal demodulation method applied to automatic detection of early mechanical fault and a new frequency spectrum zoom method are introduced. Given the deficiency of two channel pyramid decomposition, a three channel signal pyramid decomposition scheme is put forward as the supplement of two channel pyramid decomposition. In addition, referring to the stationary wavelet packet decomposition algorithm, a new vibration signal stationary filterbank decomposition method based on QMF is proposed. The analysis results on simulated and measured data show that the filtering performance of the stationary filterbank decomposition method based on QMF is better than that of the stationary wavelet packet decomposition method.
Thirdly, weak impulsive response signal feature extraction method based on continuous wavelet filtering is studied. From theoretical and simulated experimental studies, it is shown that the normal wavelet series base function and the normal time-scale analysis method are not suitable for feature extraction of weak impulsive response signal. Based on the uniform form of continuous wavelets suitable for feature extraction of weak impulsive signal, a new frequency domain compact support wavelet whose spectrum center frequency and frequency spectrum window width can be easily adjusted is constructed. The simulated data show that this wavelet filter can effectively enhance the impulsive signal feature when the selected wavelet parameters are reasonable. On how to rapidly design optimal frequency domain compact support wavelet suitable for weak impulsive signal feature extraction, the adaptive design method based on kurtosis as optimal objective function and using genetic algorithm to optimize parameters is proposed. In addition, the adaptive wavelet preprocessing method is applied to second order cyclostationary signal demodulation analysis of the weak impulsive modulation signal. The improved spectrum correlation density demodulation analysis method based on the preprocessing of optimal frequency domain compact support wavelet is first proposed. The analysis results on simulated and measured rolling element bearing data show that the improved method not only can effectively demodulate the fault feature frequency of weak periodic fault impulsive signal, but also can greatly reduce the computing burden and improve the practicability of second order cyclostationary signal demodulation method.
Fourthly, empirical mode decomposition (EMD) method on vibration signal is studied. In order to solve the problem of endpoint effects in empirical mode decomposition process, a new endpoint extreme value extrapolation method based on the comparison of waveforms is proposed. The analysis results on simulated and measured rotor vibration data show that, for EMD decomposition of the regular signal, using the new method can effectively reduce the negative influences of endpoint envelope error to EMD decomposition result and obtain true IMF components. For possible appearance of mode mixture in EMD decomposition, it is pointed out that various certain energy level irregular stationary and non-stationary additional noises are the source of mode mixture. Based on the above conclusion and understanding of the EMD method in nature, a novel adaptive filtering based mode demixing method is proposed. The analysis results on simulated and measured gear vibration data show that this new mode demixing method can effectively reduce the influence of mode mixture on decomposition result.
Finally, Blind vibration signal processing method and its application is studied. By analyzing implement theory of frequency domain multi-channel blind deconvolution algorithm, a multi-channel vibration signal blind feature extraction method based on the combination of stationary filterbank decomposition filtering and ICA algorithm is proposed. This new blind feature extraction method is applied to extract turbocharger shaft rotating frequency feature of locomotive diesel turbocharger vibration signal in serious background noise environment. The result shows that using this method can effectively extract shaft rotating frequency feature of the weak turbocharger vibration signal. Given the deficiency of normal single channel vibration signal blind deconvolution algorithm—minimum entropy blind deconvolution algorithm, a new single channel vibration signal blind deconvolution algorithm based on ICA is proposed. The results on simulated and measured vibration data show that the new algorithm has more rapid velocity of convergence, better robustness and can more effectively extracts weak impulsive response signal feature.
1)研究了基于STFT的振动信号解调及频谱细化分析方法及其应用。首次对基于STFT的振动信号解调方法的原理和影响其解调性能的各种因素进行了严格的理论分析,指出该解调方法实质是基于复解析带通滤波的Hilbert变换解调法。首次从数学上严格证明了在利用Hilbert变换进行包络解调分析时,只要带通滤波器通带范围包括调制信号的部分频率成分,就可解调出被调制信号的周期成分。基于以上理论分析,给出了实用的基于STFT的自适应振动信号解调新算法。针对复杂噪声环境下微弱周期性故障信号特征的检测问题,提出了奇异值分解降噪和STFT解调相结合的检测新方法。此外,将STFT引入到信号频谱细化分析,提出了基于STFT的无需频率成份调整的信号频谱细化分析新方法。
2)研究了基于滤波器组理论的振动信号处理方法。通过分析小波与滤波器组的关系,指出机械故障诊断领域所应用的小波(包)分解实质是利用共轭镜像对称滤波器组(CQMF)对信号进行分解。针对目前小波(包)分解方法用于振动信号分析时存在的不足,首次提出了基于正交镜像对称滤波器组(QMF)的振动信号分解方法,并构造了一种具有线性相位的两通道QMF滤波器组。与同阶的小波滤波器相比,该QMF滤波器不仅滤波性能更优,而且其滤波系数的求取更加便捷。为了解决常规两通道滤波器组分解算法中存在的子带信号组频带错位问题,引入了无频带错位的QMF滤波器组分解算法,基于此分解算法,提出了用于早期故障自动检测的振动信号解调新方法和自适应频谱细化方法。鉴于信号两通道塔形分解在实际振动信号分析中存在的不足,首次提出了信号三通道塔形分解方案作为其补充,并给出了相应的分解算法。此外,推广了现有的平稳小波包分解算法,首次提出了基于QMF滤波器组的平稳滤波器组分解算法,仿真和实测振动信号分析结果表明,该算法与平稳小波包分解算法相比具有更优的滤波性能。
3)研究了基于连续小波滤波器的微弱冲击信号特征提取方法。理论分析和仿真分析结果表明,信息工程领域中常用系列连续小波基及其常规的时间—尺度分析方法不适合微弱冲击信号的特征提取。基于适合微弱冲击信号特征提取的连续小波滤波器的统一形式,构造了一种易实现小波频谱中心频率和频窗宽度调整的频域紧支小波滤波器。仿真信号分析结果表明,当该小波滤波器参数选择合理时,可以有效地增强微弱冲击信号的冲击特征。关于如何快速地设计出适合微弱冲击信号特征提取的最优频域紧支小波滤波器问题,提出了以峭度系数为优化目标利用遗传算法进行寻优的自适应设计方法。研究了自适应小波预处理方法在弱冲击调制类二阶循环平稳信号解调中的应用,首次提出了基于最优频域紧支小波滤波器预处理的谱相关密度解调分析新方法。仿真和实测振动信号的分析结果均表明,该方法不仅可以有效地解调出微弱周期性故障冲击信号的故障特征频率,而且大大削减了原始常规方法的计算量,提高了二阶循环平稳信号解调方法的实用性。
4)研究了振动信号的EMD处理方法。提出了基于波形相似度比较的端点极值延拓新方法用于解决EMD分解过程中存在的端点效应问题。仿真信号和实测转子失衡故障振动信号分析结果表明,对于规则信号的EMD分解,利用该方法进行端点延拓,可以有效地避免端点处包络误差对分解结果产生的不利影响,得到准确的IMF分量。针对EMD分解过程中可能存在的模态混叠现象,分析了产生模态混叠现象的原因,指出原始信号中存在的一定能量大小的各种非规则的平稳和非平稳噪声是产生模态混叠的根源,并基于此结论和对EMD分解方法本质的认识,提出了基于自适应滤波的模态混叠消除新方法。仿真信号和实测齿轮故障振动信号分析结果均表明,该方法可以比较有效地消除模态混叠现象。
5)研究了振动信号的盲处理方法及其应用。通过对多通道MBD频域实现方法的剖析,提出了一种适合振动信号特征提取要求的多通道振动信号盲处理新方法—平稳滤波器组分解滤波和ICA算法相结合的振动信号盲处理方法,并将该方法应用于强噪声环境下机车柴油机增压器转频振动信号特征的提取工作,取得了令人十分满意的效果。针对目前常用单通道振动信号盲解卷算法—最小熵解卷(MED)算法在应用中存在的不足,提出了一种新的单通道振动信号盲解卷算法—基于ICA的盲解卷方法。仿真信号和实测振动信号分析的结果均表明,该方法与最小熵解卷(MED)算法相比不仅收敛速度快、鲁棒性强,而且在提取强噪声环境下微弱冲击信号特征方面,特征提取效果更明显。
Mechanical fault diagnostics is an integrated technical subject based on mechanics. The key of mechanical fault diagnosis is how to extract the fault feature from mechanical fault vibration signals which are non-gaussian and non-stationary signals. Recently, in order to meet the need of early detection and diagnosis on mechanical faults, non-gaussian and non-stationary signal processing methods attract extensive attention in the area of mechanical fault diagnosis. How to creatively apply non-gaussian and non-stationary signal processing theory to solve the problems about signal denoising and feature extraction is one of the important research issues in the area of mechanical fault diagnosis. Based on the above request, this dissertation does some researches on non-gaussian and non-stationary signal processing methods suitable for mechanical fault feature extraction. The main research work by the authors is introduced briefly as follows:
Firstly, vibration signal demodulation analysis and frequency spectrum Zoom-FFT technologies based on STFT are studied. From the point of view of signal filtering, the principle of vibration signal demodulation method based on STFT is described and some factors affecting the demodulating performance are discussed. Through theoretical analysis, it is pointed out that the vibration signal demodulation method based on STFT is a special demodulation method based on complex analytic band-pass filtering and Hilbert transform. It is theoretically proven that the signal, which has the same period with the modulated signal, can be restored by demodulation analysis if some frequency elements of the modulated signal are located in pass range of a complex analytic band-pass filter. Based on the above analysis, the novel practical STFT based adaptive vibration signal demodulation algorithm is proposed. About the problem of weak periodic fault feature extraction in complicated background noise environment, a new detection method based on the combination of singular value decomposition denoising and STFT demodulation technique is put forward. In addition, an improved STFT based frequency spectrum Zoom-FFT technology is proposed.
Secondly, vibration signal processing technologies based on filterbank theory are studied. For overcoming the deficiency of current discrete wavelet and wavelet packet transform based vibration signal decomposition method, the decomposition method based on quadrature mirror filterbank (QMF) is first proposed and a two channel QMF with linear phase is constructed. Compared with the wavelet filters, constructed QMF filters with the same filter order as the wavelet filters have better filtering performance and the filter coefficients can be obtained more easily. In order to solve the problem of the frequency band derangement in the normal two channel filter band decomposition algorithm, an improved QMF decomposition algorithm is proposed. Based on the improved algorithm, a new vibration signal demodulation method applied to automatic detection of early mechanical fault and a new frequency spectrum zoom method are introduced. Given the deficiency of two channel pyramid decomposition, a three channel signal pyramid decomposition scheme is put forward as the supplement of two channel pyramid decomposition. In addition, referring to the stationary wavelet packet decomposition algorithm, a new vibration signal stationary filterbank decomposition method based on QMF is proposed. The analysis results on simulated and measured data show that the filtering performance of the stationary filterbank decomposition method based on QMF is better than that of the stationary wavelet packet decomposition method.
Thirdly, weak impulsive response signal feature extraction method based on continuous wavelet filtering is studied. From theoretical and simulated experimental studies, it is shown that the normal wavelet series base function and the normal time-scale analysis method are not suitable for feature extraction of weak impulsive response signal. Based on the uniform form of continuous wavelets suitable for feature extraction of weak impulsive signal, a new frequency domain compact support wavelet whose spectrum center frequency and frequency spectrum window width can be easily adjusted is constructed. The simulated data show that this wavelet filter can effectively enhance the impulsive signal feature when the selected wavelet parameters are reasonable. On how to rapidly design optimal frequency domain compact support wavelet suitable for weak impulsive signal feature extraction, the adaptive design method based on kurtosis as optimal objective function and using genetic algorithm to optimize parameters is proposed. In addition, the adaptive wavelet preprocessing method is applied to second order cyclostationary signal demodulation analysis of the weak impulsive modulation signal. The improved spectrum correlation density demodulation analysis method based on the preprocessing of optimal frequency domain compact support wavelet is first proposed. The analysis results on simulated and measured rolling element bearing data show that the improved method not only can effectively demodulate the fault feature frequency of weak periodic fault impulsive signal, but also can greatly reduce the computing burden and improve the practicability of second order cyclostationary signal demodulation method.
Fourthly, empirical mode decomposition (EMD) method on vibration signal is studied. In order to solve the problem of endpoint effects in empirical mode decomposition process, a new endpoint extreme value extrapolation method based on the comparison of waveforms is proposed. The analysis results on simulated and measured rotor vibration data show that, for EMD decomposition of the regular signal, using the new method can effectively reduce the negative influences of endpoint envelope error to EMD decomposition result and obtain true IMF components. For possible appearance of mode mixture in EMD decomposition, it is pointed out that various certain energy level irregular stationary and non-stationary additional noises are the source of mode mixture. Based on the above conclusion and understanding of the EMD method in nature, a novel adaptive filtering based mode demixing method is proposed. The analysis results on simulated and measured gear vibration data show that this new mode demixing method can effectively reduce the influence of mode mixture on decomposition result.
Finally, Blind vibration signal processing method and its application is studied. By analyzing implement theory of frequency domain multi-channel blind deconvolution algorithm, a multi-channel vibration signal blind feature extraction method based on the combination of stationary filterbank decomposition filtering and ICA algorithm is proposed. This new blind feature extraction method is applied to extract turbocharger shaft rotating frequency feature of locomotive diesel turbocharger vibration signal in serious background noise environment. The result shows that using this method can effectively extract shaft rotating frequency feature of the weak turbocharger vibration signal. Given the deficiency of normal single channel vibration signal blind deconvolution algorithm—minimum entropy blind deconvolution algorithm, a new single channel vibration signal blind deconvolution algorithm based on ICA is proposed. The results on simulated and measured vibration data show that the new algorithm has more rapid velocity of convergence, better robustness and can more effectively extracts weak impulsive response signal feature.
引文
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