基于自适应振动信号处理的旋转机械故障诊断研究
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摘要
在旋转机械设备故障诊断研究中，故障特征提取和模式识别关系到故障诊断的可靠性和准确性，因此是旋转机械故障诊断研究中的关键问题。利用轴承和齿轮的振动信号对其工作状态进行监测和诊断是目前旋转机械故障监测和诊断研究中最常用的方法。本学位论文应用经验模态分解、总体平均经验模态分解和局部均值分解等信号处理方法进行故障特征提取，并应用支持向量机进行故障模式识别。其主要内容如下：
1、基于经验模态分解的轴承和齿轮故障诊断研究。
针对旋转机械设备的工作环境恶劣难以提取故障频率的实际情况，应用奇异值差分谱理论对经验模态分解得到的本征模式分量进行消噪，更好地得到了轴承故障频率；通过计算经验模态分解所得到的本征模式分量的能量熵，在能量域角度找到了齿轮的故障特征，并进一步应用支持向量机对其进行模式识别，通过实例验证此方法的可行性；通过计算经验模态分解所得到的本征模式分量的奇异值熵，找到了齿轮的故障特征，并进一步应用支持向量机对其进行模式识别，通过实例验证此方法的有效性和在小样本情况下的可行性。
2、基于总体平均经验模态分解的齿轮故障诊断研究。
针对齿轮振动信号的非平稳特征和现实中难以获得大量典型故障样本的实际情况，提出了基于总体平均经验模态分解和支持向量机的齿轮故障诊断方法。首先通过总体平均经验模态分解方法将非平稳的原始加速度振动信号分解成若干个平稳的本征模式分量；齿轮发生不同的故障时，在不同频带内的信号能量值会发生改变，故可通过计算不同振动信号的能量熵判断是否发生故障；从包含有主要故障信息的本征模式分量中提取出来的能量特征作为输入建立支持向量机，判断齿轮的工作状态和故障类型。实验结果表明，文中提出的方法能有效地应用于齿轮的故障诊断。
提出了一种基于总体平均经验模态分解奇异值熵和支持向量机的齿轮故障诊断方法。首先通过总体平均经验模态分解方法将非平稳的原始加速度振动信号分解成若干个平稳的本征模式分量，将得到的若干个本征模式分量自动形成初始特征向量矩阵；然后对该矩阵进行奇异值分解，提取其奇异值作为故障特征向量，并对其进行归一化，求得奇异值熵，根据奇异值熵值大小可以判断齿轮的故障类型；将奇异值故障特征向量作为支持向量机的输入，判断齿轮的工作状态和故障类型。实验结果表明，即使在小样本情况下，基于奇异值分解和支持向量机的故障诊断方法仍能有效地识别齿轮的工作状态和故障类型。
3、基于局部均值分解的轴承和齿轮故障诊断研究。
首先对轴承的振动信号进行随机共振消噪，然后对降噪振动信号进行局部均值分解，成功地提取出了轴承故障特征；应用局部均值分解对齿轮振动信号进行分解得到若干个乘积分量，求取每一个乘积分量的近似熵，进而找到故障特征向量，最后应用支持向量机对其进行模式识别。通过一故障诊断实例对此方法的可行性和有效性进行了验证，并与神经网络在训练时间和分类准确性方面进行了对比；通过求取经过LMD分解所得乘积分量的Lempel-Ziv指标获得轴承故障特征向量，进行了有效准确的故障诊断。
4、基于极值域均值模态分解的滚动轴承和转子系统故障诊断。
针对滚动轴承损伤性故障的故障诊断问题，提出了基于极值域均值模态分解的故障诊断方法，进行了故障特征频率的提取。首先将原始信号分解成若干个本征模式分量，然后通过计算各个本征模式分量与原始信号的相关系数确定包含故障特征信息的主要成分，除去虚假分量。最后针对主要成分的本征模式分量进行Hilbert包络解调提取故障特征，即轴承的损伤性故障特征。通过工程实例信号的分析结果以及与经验模式分解方法的对比均表明，该方法能够较快地提取轴承的故障特征。
针对转子不平衡故障和滚动轴承微弱损伤性故障的复合故障诊断问题，提出了基于第二代小波和极值域均值模态分解的故障诊断方法，进行了复合故障的耦合特征分离和故障特征频率的提取。该方法首先应用第二代小波对原始信号进行分解与重构；然后针对分解与重构出的低频信号进行频谱分析提取低频非调制故障特征；最后针对高频共振调制信号进行解调分析，以准确提取调制故障特征。通过工程实例信号的分析结果表明，该方法能够提取转子系统的复合故障特征。
5、总结全文并提出了研究展望。
Fault feature extraction and pattern recognition is the most crucial problem for thereliability and accuracy in the fault diagnosis of rotating machineries. The vibration signals ofbearings and gears are employed in monitoring and diagnosing, which is the common usedmethod in the study of mechanical fault monitoring and diagnosis. Apply empirical modedecomposition, ensemble empirical mode decomposition and local mean decomposition, toextract the fault feature and to recognize the fault pattern using support vector machines inthis dissertation. The main research works can be described as follows:
1. Fault diagnosis methods of bearings and gears based on empirical modedecomposition.
In view of the strong background noise involved in the fault signals of rotatingmachineries and the difficulty to obtain fault frequencies in practice, a fault diagnosis scheme,which is based on empirical mode decomposition (EMD) and difference spectrum theory ofsingular value, is put forward in this dissertation. On the basis of difference spectrum theory,de-noising and reconstruction can be done to some intrinsic mode functions (IMFs) in order toget its frequency spectrum more accurate. To identify the fault pattern and condition, energyfeature extracted from a number of IMFs that contained the most dominant fault informationcould serve as input vectors of support vector machine. Practical examples show that thediagnosis approach put forward in this paper can identify gear fault patterns effectively.Singular value entropies extracted from a number of IMFs that contained the most dominantfault information could serve as input vectors of support vector machine. Practical examplesshow that the diagnosis approach put forward in this paper can identify gear fault patternseffectively even when the numbers of samples is small.
2. Fault diagnosis methods of gears based on ensemble empirical mode decomposition.
In view of the non-stationary features of vibration signals of gear and the difficulty toobtain a large number of fault samples in practice, a fault diagnosis scheme based onensemble empirical mode decomposition (EEMD) energy entropy and support vector machineis put forward in this paper. Firstly, original acceleration vibration signals are decomposedinto a finite number of stationary IMFs; the energy of vibration signal will change in differentfrequency bands when fault occurs. Therefore, to identify the fault pattern and condition,energy feature extracted from a number of IMFs that contained the most dominant faultinformation could serve as input vectors of support vector machine. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patternseffectively.
A fault diagnosis scheme based on EEMD entropy of singular values and support vectormachine is put forward in this paper. Firstly, original acceleration vibration signals aredecomposed into a finite number of stationary IMFs, and the initial feature vector matrixes areformed automatically by the IMFs; secondly, to apply the singular value decomposition to theinitial feature vector matrixes, the singular values, as the fault characteristic vectors, areobtained. By normalizing the vectors and getting the entropies of singular values, faultpatterns and conditions of gear cases can be identified. Singular values extracted from anumber of IMFs that contained the most dominant fault information could serve as inputvectors of support vector machine. Practical examples show that the diagnosis approach putforward in this paper can identify gear fault patterns effectively even when the numbers ofsamples is small.
3. Fault diagnosis methods of bearings and gears based on local mean decomposition.
Firstly, do de-noising vibration signals of bearings by employing stochastic resonance.Secondly, the vibration signals are decomposed by local mean decomposition (LMD) toextract fault patterns successfully. By local mean decomposition, the original accelerationvibration signals can be decomposed into a finite number of product functions (PF). Tocalculate the approximate entropies of product functions, the fault feature vectors, whichcould serve as input vectors of support vector machine to identify fault patterns andconditions, can be found. Through a fault diagnosis example the feasibility and effectivenessof this method is verified. Aspects in the training time and classification accuracy werecompared with neural network. The bearing fault characteristic can be extracted successfullyby calculating the Lempel-Ziv indexes of PFs.
4. Fault diagnosis methods of bearings and rotor systems based on extremum field meanmode decomposition.
Aiming at the fault of rolling bearings, based on the extremum field mean modedecomposition, was proposed to separate the coupling features of the fault and to extract thefrequency of fault signals. Firstly original signals were decomposed to obtain several IMFs byextremum field mean mode decomposition (EMMD). Then main components are confirmedby calculating the correlation coefficient of every IMF and original signal, and falsecomponents were removed at the same time.Finally high-frequency modulate feature of therolling bearing was extracted by Hilbert envelope demodulation from the component of maincomponents. Research results of engineering signals and the contrasts to EMD, both indicate that the method can extract the fault feature of rolling bearings quickly.
Aiming at the composite fault of the rotor failure and weak roller bearing fault, based onthe second generation wavelet and the extremism field mean mode decomposition, and wasproposed to separate the coupling features of the composite fault and to extract the frequencyof fault signals. Firstly original signals were decomposed and reconstructed by using thesecond generation wavelet. Then non-modulation low-frequency fault feature was extractedby using FFT to the low-frequency signals from the decomposition and restruction of originalsignals. At the last, the high-frequency modulated signals from the decomposition andrestruction of original were analyzed by envelop demodulation based on EMMD, by whichthe modulated fault feature was extracted. Research results of engineering signals indicate thatthe method can extract the composite fault feature of rotor system.
5. Summing up and puts forward the research prospect.
1、基于经验模态分解的轴承和齿轮故障诊断研究。
针对旋转机械设备的工作环境恶劣难以提取故障频率的实际情况，应用奇异值差分谱理论对经验模态分解得到的本征模式分量进行消噪，更好地得到了轴承故障频率；通过计算经验模态分解所得到的本征模式分量的能量熵，在能量域角度找到了齿轮的故障特征，并进一步应用支持向量机对其进行模式识别，通过实例验证此方法的可行性；通过计算经验模态分解所得到的本征模式分量的奇异值熵，找到了齿轮的故障特征，并进一步应用支持向量机对其进行模式识别，通过实例验证此方法的有效性和在小样本情况下的可行性。
2、基于总体平均经验模态分解的齿轮故障诊断研究。
针对齿轮振动信号的非平稳特征和现实中难以获得大量典型故障样本的实际情况，提出了基于总体平均经验模态分解和支持向量机的齿轮故障诊断方法。首先通过总体平均经验模态分解方法将非平稳的原始加速度振动信号分解成若干个平稳的本征模式分量；齿轮发生不同的故障时，在不同频带内的信号能量值会发生改变，故可通过计算不同振动信号的能量熵判断是否发生故障；从包含有主要故障信息的本征模式分量中提取出来的能量特征作为输入建立支持向量机，判断齿轮的工作状态和故障类型。实验结果表明，文中提出的方法能有效地应用于齿轮的故障诊断。
提出了一种基于总体平均经验模态分解奇异值熵和支持向量机的齿轮故障诊断方法。首先通过总体平均经验模态分解方法将非平稳的原始加速度振动信号分解成若干个平稳的本征模式分量，将得到的若干个本征模式分量自动形成初始特征向量矩阵；然后对该矩阵进行奇异值分解，提取其奇异值作为故障特征向量，并对其进行归一化，求得奇异值熵，根据奇异值熵值大小可以判断齿轮的故障类型；将奇异值故障特征向量作为支持向量机的输入，判断齿轮的工作状态和故障类型。实验结果表明，即使在小样本情况下，基于奇异值分解和支持向量机的故障诊断方法仍能有效地识别齿轮的工作状态和故障类型。
3、基于局部均值分解的轴承和齿轮故障诊断研究。
首先对轴承的振动信号进行随机共振消噪，然后对降噪振动信号进行局部均值分解，成功地提取出了轴承故障特征；应用局部均值分解对齿轮振动信号进行分解得到若干个乘积分量，求取每一个乘积分量的近似熵，进而找到故障特征向量，最后应用支持向量机对其进行模式识别。通过一故障诊断实例对此方法的可行性和有效性进行了验证，并与神经网络在训练时间和分类准确性方面进行了对比；通过求取经过LMD分解所得乘积分量的Lempel-Ziv指标获得轴承故障特征向量，进行了有效准确的故障诊断。
4、基于极值域均值模态分解的滚动轴承和转子系统故障诊断。
针对滚动轴承损伤性故障的故障诊断问题，提出了基于极值域均值模态分解的故障诊断方法，进行了故障特征频率的提取。首先将原始信号分解成若干个本征模式分量，然后通过计算各个本征模式分量与原始信号的相关系数确定包含故障特征信息的主要成分，除去虚假分量。最后针对主要成分的本征模式分量进行Hilbert包络解调提取故障特征，即轴承的损伤性故障特征。通过工程实例信号的分析结果以及与经验模式分解方法的对比均表明，该方法能够较快地提取轴承的故障特征。
针对转子不平衡故障和滚动轴承微弱损伤性故障的复合故障诊断问题，提出了基于第二代小波和极值域均值模态分解的故障诊断方法，进行了复合故障的耦合特征分离和故障特征频率的提取。该方法首先应用第二代小波对原始信号进行分解与重构；然后针对分解与重构出的低频信号进行频谱分析提取低频非调制故障特征；最后针对高频共振调制信号进行解调分析，以准确提取调制故障特征。通过工程实例信号的分析结果表明，该方法能够提取转子系统的复合故障特征。
5、总结全文并提出了研究展望。
Fault feature extraction and pattern recognition is the most crucial problem for thereliability and accuracy in the fault diagnosis of rotating machineries. The vibration signals ofbearings and gears are employed in monitoring and diagnosing, which is the common usedmethod in the study of mechanical fault monitoring and diagnosis. Apply empirical modedecomposition, ensemble empirical mode decomposition and local mean decomposition, toextract the fault feature and to recognize the fault pattern using support vector machines inthis dissertation. The main research works can be described as follows:
1. Fault diagnosis methods of bearings and gears based on empirical modedecomposition.
In view of the strong background noise involved in the fault signals of rotatingmachineries and the difficulty to obtain fault frequencies in practice, a fault diagnosis scheme,which is based on empirical mode decomposition (EMD) and difference spectrum theory ofsingular value, is put forward in this dissertation. On the basis of difference spectrum theory,de-noising and reconstruction can be done to some intrinsic mode functions (IMFs) in order toget its frequency spectrum more accurate. To identify the fault pattern and condition, energyfeature extracted from a number of IMFs that contained the most dominant fault informationcould serve as input vectors of support vector machine. Practical examples show that thediagnosis approach put forward in this paper can identify gear fault patterns effectively.Singular value entropies extracted from a number of IMFs that contained the most dominantfault information could serve as input vectors of support vector machine. Practical examplesshow that the diagnosis approach put forward in this paper can identify gear fault patternseffectively even when the numbers of samples is small.
2. Fault diagnosis methods of gears based on ensemble empirical mode decomposition.
In view of the non-stationary features of vibration signals of gear and the difficulty toobtain a large number of fault samples in practice, a fault diagnosis scheme based onensemble empirical mode decomposition (EEMD) energy entropy and support vector machineis put forward in this paper. Firstly, original acceleration vibration signals are decomposedinto a finite number of stationary IMFs; the energy of vibration signal will change in differentfrequency bands when fault occurs. Therefore, to identify the fault pattern and condition,energy feature extracted from a number of IMFs that contained the most dominant faultinformation could serve as input vectors of support vector machine. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patternseffectively.
A fault diagnosis scheme based on EEMD entropy of singular values and support vectormachine is put forward in this paper. Firstly, original acceleration vibration signals aredecomposed into a finite number of stationary IMFs, and the initial feature vector matrixes areformed automatically by the IMFs; secondly, to apply the singular value decomposition to theinitial feature vector matrixes, the singular values, as the fault characteristic vectors, areobtained. By normalizing the vectors and getting the entropies of singular values, faultpatterns and conditions of gear cases can be identified. Singular values extracted from anumber of IMFs that contained the most dominant fault information could serve as inputvectors of support vector machine. Practical examples show that the diagnosis approach putforward in this paper can identify gear fault patterns effectively even when the numbers ofsamples is small.
3. Fault diagnosis methods of bearings and gears based on local mean decomposition.
Firstly, do de-noising vibration signals of bearings by employing stochastic resonance.Secondly, the vibration signals are decomposed by local mean decomposition (LMD) toextract fault patterns successfully. By local mean decomposition, the original accelerationvibration signals can be decomposed into a finite number of product functions (PF). Tocalculate the approximate entropies of product functions, the fault feature vectors, whichcould serve as input vectors of support vector machine to identify fault patterns andconditions, can be found. Through a fault diagnosis example the feasibility and effectivenessof this method is verified. Aspects in the training time and classification accuracy werecompared with neural network. The bearing fault characteristic can be extracted successfullyby calculating the Lempel-Ziv indexes of PFs.
4. Fault diagnosis methods of bearings and rotor systems based on extremum field meanmode decomposition.
Aiming at the fault of rolling bearings, based on the extremum field mean modedecomposition, was proposed to separate the coupling features of the fault and to extract thefrequency of fault signals. Firstly original signals were decomposed to obtain several IMFs byextremum field mean mode decomposition (EMMD). Then main components are confirmedby calculating the correlation coefficient of every IMF and original signal, and falsecomponents were removed at the same time.Finally high-frequency modulate feature of therolling bearing was extracted by Hilbert envelope demodulation from the component of maincomponents. Research results of engineering signals and the contrasts to EMD, both indicate that the method can extract the fault feature of rolling bearings quickly.
Aiming at the composite fault of the rotor failure and weak roller bearing fault, based onthe second generation wavelet and the extremism field mean mode decomposition, and wasproposed to separate the coupling features of the composite fault and to extract the frequencyof fault signals. Firstly original signals were decomposed and reconstructed by using thesecond generation wavelet. Then non-modulation low-frequency fault feature was extractedby using FFT to the low-frequency signals from the decomposition and restruction of originalsignals. At the last, the high-frequency modulated signals from the decomposition andrestruction of original were analyzed by envelop demodulation based on EMMD, by whichthe modulated fault feature was extracted. Research results of engineering signals indicate thatthe method can extract the composite fault feature of rotor system.
5. Summing up and puts forward the research prospect.
引文
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