柴油机振动信号特征提取与故障诊断方法研究
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摘要
柴油机在工农业生产中获得了广泛应用。作为动力机械，其运行状态好坏，直接影响到整个机组的工作性能。因此，研究柴油机故障诊断技术和方法，对柴油机进行状态检测和故障诊断、确保柴油机工作正常具有重要意义。
柴油机故障诊断技术是通过分析处理柴油机运行时的状态信息，定量识别其技术状态、并预测异常故障状态的一门多学科交叉的综合技术。柴油机缸盖振动信号中包含着丰富的工作状态信息，利用缸盖振动信号诊断柴油机故障是一种有效方法。从缸盖振动信号中提取故障特征以及根据提取的特征量对故障类型作出正确判断是故障诊断的两个关键。本文从工程应用的角度出发，综合应用测试技术、小波分析、经验模式分解、混沌数值特征、BP神经网络、支持向量机等理论，对柴油机缸盖振动信号中故障特征信息的提取、柴油机故障状态的识别诊断这两个关键问题进行了系统地研究，为进一步提高柴油机故障诊断水平提供了理论支持。主要研究内容及结论如下：
（1）建立了柴油机缸盖振动信号采集实验平台。以S195柴油机为测试对象，采集了柴油机在正常及不同故障状态下缸盖振动信号样本，为柴油机振动信号的特征提取和诊断研究提供了数据支持。
（2）研究了小波分析在缸盖振动信号特征提取中的应用。分析了柴油机不同状态下的缸盖振动信号的小波包能量分布以及连续小波尺度—能量分布，结果表明：小波包能量分布、连续小波尺度—能量分布均可作为表征柴油机不同工作状态的特征参数。考虑到缸盖振动信号的时域特征参数，本文提出了用小波包能量分布、小波包能量熵分布、小波包能量分布结合信号时域特征、连续小波尺度能量分布等4种构建缸盖振动信号特征向量的方法。
（3）研究了EMD方法在柴油机缸盖振动信号特征提取中的应用。针对缸盖振动信号中出现的脉冲噪声干扰，提出了一种改进的基于EMD的信号降噪方法。将EMD与边际谱、自功率谱以及AR模型谱估计方法相结合，分析了用这三类谱参数表征柴油机不同工作状态的可行性。考虑到缸盖振动信号的时域和频域特征，提出了时域特征量与IMF能量组合、时域特征量与Hilbert边际谱组合、时域特征量与AR模型谱组合等3种构建缸盖振动信号特征向量的方法。
（4）研究了混沌数值特征—关联维数、最大Lyaponov指数的分析计算方法，用混沌数值特征对柴油机燃油系出现故障时的缸盖振动信号进行了分析。结果表明，由柴油机缸盖振动信号直接求取关联维值，不能识别柴油机燃油系故障；由柴油机缸盖振动信号直接求取最大Lyaponov指数值可以判断柴油机燃油系是否异常。针对直接用缸盖振动信号关联维值诊断柴油机故障的不足，提出将小波包分解和关联维数相结合诊断柴油机燃油系故障的方法。结果表明，该方法可用于判断柴油机燃油系工作正常与否。
（5）研究了用BP神经网络和支持向量机对柴油机故障识别的方法。利用4种基于小波分析构建特征量的方法（小波包能量分布、小波包能量熵、小波包能量分布结合时域特征、连续小波能量分布）分别对柴油机正常工况、排气门间隙异常、进气门间隙异常、供油提前角异常、喷油压力异常等5类工况构建特征向量。用BP网络进行诊断分类，其平均识别正确率分别为：79.39%、85.67%、87.03%和91.15%；用支持向量机进行诊断分类，其平均识别正确率分别为：82.67%、90.33%、87%和92.50%。诊断结果表明：4种基于小波分析构建特征向量的方法，均可用于柴油机缸盖振动信号特征向量的构建。用连续小波尺度能量分布构建特征向量，用支持向量机进行分类诊断可以获得最好的诊断效果。对于同一方法构建的特征向量，支持向量机的分类诊断效果总体优于BP网络的诊断效果。
（6）利用3种基于EMD构建特征向量的方法（时域特征参数与IMF能量组合、时域特征参数与Hilbert边际谱组合、时域特征参数与AR模型谱组合）对柴油机正常工况、排气门间隙异常、进气门间隙异常、供油提前角异常、喷油压力异常等5类工况构建特征向量。用BP网络进行诊断分类，其平均识别正确率分别为：80.2%、85.5%和87.8%；用支持向量机进行诊断分类，其平均识别正确率分别为：80.83%、83.67%和91.0%。诊断结果表明：3种基于EMD构建特征向量的方法，均可用于柴油机缸盖振动信号特征向量的构建；用AR模型谱和时域特征参数构建特征向量，用支持向量机进行分类诊断可以获得最好的诊断效果。
As power machinery, the diesel engine is widely used in industrial and agriculturalproduction. Its operation status directly affects the performance of the entire unit. Therefore, itis of important significance to monitor the operation status and carry out fault diagnosis sothat the diesel engine operates in the normal state.
Diesel engine fault diagnosis technology is an integrated technology based onmultidisciplinary which identifies the status of diesel engine and predicts the abnormal faultcondition by analyzing and processing the real-time status information of the diesel engine.Diesel engine cylinder head vibration signals contain a lot of information of operating status,so it is an effective method to diagnose diesel engine fault using cylinder head vibrationsignals.
Extracting features from the cylinder head vibration signals and fault type recognitionaccording to the extracted fault features are the two key topics in the field of fault diagnosis ofdiesel engine. From the engineering application perspective, this thesis systematicallyinvestigated the fault feature extraction and fault type recognition methods of the dieselengine applying testing technology, wavelet analysis, empirical mode decomposition, chaoticnumerical characteristics, BP neural network, and support vector machine theory. The mainresearch work is as follows:
(1) A diesel engine cylinder head vibration signal acquisition experimental platform wascomposed. The diesel engine cylinder head vibration signals in normal and different faultconditions of S195were collected, which can be used for feature extraction and faultdiagnosis of diesel engine fault vibration signal.
(2) This thesis has studied application of the wavelet analysis in the cylinder headvibration signal feature extraction. The wavelet packet energy distribution and the continuouswavelet scale energy distribution of diesel engine cylinder head vibration signal underdifferent states were analyzed, and the results showed that wavelet packet energy distributionand continuous wavelet scale energy distribution could be characterized as fault features ofthe diesel engine failure. Taking into account the time-domain characteristic parameters of thecylinder head vibration signals, this thesis put forward four methods to compose diesel engine fault feature vector, including the wavelet packet energy distribution, the wavelet packetenergy entropy distribution, the wavelet packet energy distribution combined with signal timedomain characteristics, and the continuous wavelet scale energy distribution.
(3) Feature extraction of diesel engine cylinder head vibration signals using EMD wasdiscussed. For the pulse noise interference appearing in the cylinder head vibration signal, thisthesis put forward an improved signal noise reduction method based on EMD. The thesis usedmarginal spectrum, the power spectrum and the AR model spectrum estimation methodcombined with EMD to illustrate the characterization of diesel engine fault featureinformation. Taking into account the signal time-domain characteristic and frequency domaincharacteristics, three composing diesel engine fault feature vector methods were presented.They are the time-domain features combined with IMF energy, the time-domain featurescombined with Hilbert marginal spectrum and the time-domain features combined with theAR model spectrum estimation.
(4) The chaotic numerical characteristics correlation dimensions and the analysis andcalculation methods of the maximum Lyaponov exponent were studied, and the Chaoticnumerical characteristics of the diesel engine fuel system fault vibration signal were analyzed.The results showed correlation dimension values computed with the original signals can notdistinguish whether the diesel engine fuel system operates properly while the maximumLyapunov exponents can. So this thesis put forward a method of using the wavelet packet todeal with the cylinder head vibration signal firstly, and then computing correlation dimensionvalues of the signals to diagnose fault. The results showed that the correlation dimensionvalue obtained by this method can judge diesel engine fuel system to operate properly or not.
(5) BP neural network and support vector machine method of diesel engine faultidentification were studied. Diesel engine fault feature vector were constructed using the fourmethods (wavelet packet energy distribution; wavelet packet energy entropy; wavelet packetenergy distribution combined with time-domain characteristics; continuous wavelet energydistribution) based on wavelet analysis for five types of abnormalities of diesel engine whichare the normal operating conditions, the abnormal exhaust valve clearance, the abnormalintake valve clearance, the abnormal fuel supply advance angle and the abnormal pressurefuel injection conditions. Diagnostic classification recognition accuracy rate by BP networkwere79.39%,85.67%,87.03%,91.15%respectively; and diagnostic classification recognitionaccuracy rate using support vector machine were82.67%,90.33%,87%and92.50%respectively. The fault diagnosis results showed that four types of feature vector constructingmethods based on wavelet analysis can be used in feature vector constructing for a dieselengine cylinder head vibration signal. Feature vector constructing using continuous wavelet scale energy distribution and classification diagnosis using support vector machine canachieve the best diagnosis effect. For the same method to feature vector constructing, theeffect of diagnosis classification using support vector machine is better than using BP neuralnetwork.
(6) Diesel engine fault feature vector were constructed using the three methods (thetime-domain features combined with IMF energy; the time-domain features combined withHilbert marginal spectrum; the time-domain features combined with the AR model spectrumestimation.) based on EMD for five types of abnormalities of diesel engine which are thenormal operating conditions, the abnormal exhaust valve clearance, the abnormal intake valveclearance, the abnormal fuel supply advance angle and the abnormal pressure fuel injectionconditions. Diagnostic classification recognition accuracy rate by BP neural network were80.2%,85.5%, and87.8%respectively and diagnostic classification recognition accuracy rateusing support vector machine were80.83%,83.67%and91.0%respectively. The faultdiagnosis results showed that three types of feature vector constructing methods based onEMD can be used in feature vector constructing for a diesel engine cylinder head vibrationsignal. Feature Extraction using the time-domain features combined with the AR modelspectrum estimation and classification diagnosis using support vector machine can get thebest diagnosis effect.
柴油机故障诊断技术是通过分析处理柴油机运行时的状态信息，定量识别其技术状态、并预测异常故障状态的一门多学科交叉的综合技术。柴油机缸盖振动信号中包含着丰富的工作状态信息，利用缸盖振动信号诊断柴油机故障是一种有效方法。从缸盖振动信号中提取故障特征以及根据提取的特征量对故障类型作出正确判断是故障诊断的两个关键。本文从工程应用的角度出发，综合应用测试技术、小波分析、经验模式分解、混沌数值特征、BP神经网络、支持向量机等理论，对柴油机缸盖振动信号中故障特征信息的提取、柴油机故障状态的识别诊断这两个关键问题进行了系统地研究，为进一步提高柴油机故障诊断水平提供了理论支持。主要研究内容及结论如下：
（1）建立了柴油机缸盖振动信号采集实验平台。以S195柴油机为测试对象，采集了柴油机在正常及不同故障状态下缸盖振动信号样本，为柴油机振动信号的特征提取和诊断研究提供了数据支持。
（2）研究了小波分析在缸盖振动信号特征提取中的应用。分析了柴油机不同状态下的缸盖振动信号的小波包能量分布以及连续小波尺度—能量分布，结果表明：小波包能量分布、连续小波尺度—能量分布均可作为表征柴油机不同工作状态的特征参数。考虑到缸盖振动信号的时域特征参数，本文提出了用小波包能量分布、小波包能量熵分布、小波包能量分布结合信号时域特征、连续小波尺度能量分布等4种构建缸盖振动信号特征向量的方法。
（3）研究了EMD方法在柴油机缸盖振动信号特征提取中的应用。针对缸盖振动信号中出现的脉冲噪声干扰，提出了一种改进的基于EMD的信号降噪方法。将EMD与边际谱、自功率谱以及AR模型谱估计方法相结合，分析了用这三类谱参数表征柴油机不同工作状态的可行性。考虑到缸盖振动信号的时域和频域特征，提出了时域特征量与IMF能量组合、时域特征量与Hilbert边际谱组合、时域特征量与AR模型谱组合等3种构建缸盖振动信号特征向量的方法。
（4）研究了混沌数值特征—关联维数、最大Lyaponov指数的分析计算方法，用混沌数值特征对柴油机燃油系出现故障时的缸盖振动信号进行了分析。结果表明，由柴油机缸盖振动信号直接求取关联维值，不能识别柴油机燃油系故障；由柴油机缸盖振动信号直接求取最大Lyaponov指数值可以判断柴油机燃油系是否异常。针对直接用缸盖振动信号关联维值诊断柴油机故障的不足，提出将小波包分解和关联维数相结合诊断柴油机燃油系故障的方法。结果表明，该方法可用于判断柴油机燃油系工作正常与否。
（5）研究了用BP神经网络和支持向量机对柴油机故障识别的方法。利用4种基于小波分析构建特征量的方法（小波包能量分布、小波包能量熵、小波包能量分布结合时域特征、连续小波能量分布）分别对柴油机正常工况、排气门间隙异常、进气门间隙异常、供油提前角异常、喷油压力异常等5类工况构建特征向量。用BP网络进行诊断分类，其平均识别正确率分别为：79.39%、85.67%、87.03%和91.15%；用支持向量机进行诊断分类，其平均识别正确率分别为：82.67%、90.33%、87%和92.50%。诊断结果表明：4种基于小波分析构建特征向量的方法，均可用于柴油机缸盖振动信号特征向量的构建。用连续小波尺度能量分布构建特征向量，用支持向量机进行分类诊断可以获得最好的诊断效果。对于同一方法构建的特征向量，支持向量机的分类诊断效果总体优于BP网络的诊断效果。
（6）利用3种基于EMD构建特征向量的方法（时域特征参数与IMF能量组合、时域特征参数与Hilbert边际谱组合、时域特征参数与AR模型谱组合）对柴油机正常工况、排气门间隙异常、进气门间隙异常、供油提前角异常、喷油压力异常等5类工况构建特征向量。用BP网络进行诊断分类，其平均识别正确率分别为：80.2%、85.5%和87.8%；用支持向量机进行诊断分类，其平均识别正确率分别为：80.83%、83.67%和91.0%。诊断结果表明：3种基于EMD构建特征向量的方法，均可用于柴油机缸盖振动信号特征向量的构建；用AR模型谱和时域特征参数构建特征向量，用支持向量机进行分类诊断可以获得最好的诊断效果。
As power machinery, the diesel engine is widely used in industrial and agriculturalproduction. Its operation status directly affects the performance of the entire unit. Therefore, itis of important significance to monitor the operation status and carry out fault diagnosis sothat the diesel engine operates in the normal state.
Diesel engine fault diagnosis technology is an integrated technology based onmultidisciplinary which identifies the status of diesel engine and predicts the abnormal faultcondition by analyzing and processing the real-time status information of the diesel engine.Diesel engine cylinder head vibration signals contain a lot of information of operating status,so it is an effective method to diagnose diesel engine fault using cylinder head vibrationsignals.
Extracting features from the cylinder head vibration signals and fault type recognitionaccording to the extracted fault features are the two key topics in the field of fault diagnosis ofdiesel engine. From the engineering application perspective, this thesis systematicallyinvestigated the fault feature extraction and fault type recognition methods of the dieselengine applying testing technology, wavelet analysis, empirical mode decomposition, chaoticnumerical characteristics, BP neural network, and support vector machine theory. The mainresearch work is as follows:
(1) A diesel engine cylinder head vibration signal acquisition experimental platform wascomposed. The diesel engine cylinder head vibration signals in normal and different faultconditions of S195were collected, which can be used for feature extraction and faultdiagnosis of diesel engine fault vibration signal.
(2) This thesis has studied application of the wavelet analysis in the cylinder headvibration signal feature extraction. The wavelet packet energy distribution and the continuouswavelet scale energy distribution of diesel engine cylinder head vibration signal underdifferent states were analyzed, and the results showed that wavelet packet energy distributionand continuous wavelet scale energy distribution could be characterized as fault features ofthe diesel engine failure. Taking into account the time-domain characteristic parameters of thecylinder head vibration signals, this thesis put forward four methods to compose diesel engine fault feature vector, including the wavelet packet energy distribution, the wavelet packetenergy entropy distribution, the wavelet packet energy distribution combined with signal timedomain characteristics, and the continuous wavelet scale energy distribution.
(3) Feature extraction of diesel engine cylinder head vibration signals using EMD wasdiscussed. For the pulse noise interference appearing in the cylinder head vibration signal, thisthesis put forward an improved signal noise reduction method based on EMD. The thesis usedmarginal spectrum, the power spectrum and the AR model spectrum estimation methodcombined with EMD to illustrate the characterization of diesel engine fault featureinformation. Taking into account the signal time-domain characteristic and frequency domaincharacteristics, three composing diesel engine fault feature vector methods were presented.They are the time-domain features combined with IMF energy, the time-domain featurescombined with Hilbert marginal spectrum and the time-domain features combined with theAR model spectrum estimation.
(4) The chaotic numerical characteristics correlation dimensions and the analysis andcalculation methods of the maximum Lyaponov exponent were studied, and the Chaoticnumerical characteristics of the diesel engine fuel system fault vibration signal were analyzed.The results showed correlation dimension values computed with the original signals can notdistinguish whether the diesel engine fuel system operates properly while the maximumLyapunov exponents can. So this thesis put forward a method of using the wavelet packet todeal with the cylinder head vibration signal firstly, and then computing correlation dimensionvalues of the signals to diagnose fault. The results showed that the correlation dimensionvalue obtained by this method can judge diesel engine fuel system to operate properly or not.
(5) BP neural network and support vector machine method of diesel engine faultidentification were studied. Diesel engine fault feature vector were constructed using the fourmethods (wavelet packet energy distribution; wavelet packet energy entropy; wavelet packetenergy distribution combined with time-domain characteristics; continuous wavelet energydistribution) based on wavelet analysis for five types of abnormalities of diesel engine whichare the normal operating conditions, the abnormal exhaust valve clearance, the abnormalintake valve clearance, the abnormal fuel supply advance angle and the abnormal pressurefuel injection conditions. Diagnostic classification recognition accuracy rate by BP networkwere79.39%,85.67%,87.03%,91.15%respectively; and diagnostic classification recognitionaccuracy rate using support vector machine were82.67%,90.33%,87%and92.50%respectively. The fault diagnosis results showed that four types of feature vector constructingmethods based on wavelet analysis can be used in feature vector constructing for a dieselengine cylinder head vibration signal. Feature vector constructing using continuous wavelet scale energy distribution and classification diagnosis using support vector machine canachieve the best diagnosis effect. For the same method to feature vector constructing, theeffect of diagnosis classification using support vector machine is better than using BP neuralnetwork.
(6) Diesel engine fault feature vector were constructed using the three methods (thetime-domain features combined with IMF energy; the time-domain features combined withHilbert marginal spectrum; the time-domain features combined with the AR model spectrumestimation.) based on EMD for five types of abnormalities of diesel engine which are thenormal operating conditions, the abnormal exhaust valve clearance, the abnormal intake valveclearance, the abnormal fuel supply advance angle and the abnormal pressure fuel injectionconditions. Diagnostic classification recognition accuracy rate by BP neural network were80.2%,85.5%, and87.8%respectively and diagnostic classification recognition accuracy rateusing support vector machine were80.83%,83.67%and91.0%respectively. The faultdiagnosis results showed that three types of feature vector constructing methods based onEMD can be used in feature vector constructing for a diesel engine cylinder head vibrationsignal. Feature Extraction using the time-domain features combined with the AR modelspectrum estimation and classification diagnosis using support vector machine can get thebest diagnosis effect.
引文
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