基于流形学习的滚动轴承故障诊断若干方法研究
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摘要
滚动轴承是机械设备中使用量最多的关键零部件,保证滚动轴承正常运行是设备维护工作的重要内容。但滚动轴承工作状态复杂,转速变化大,承载方式多样,运动形式多变,这些都会对滚动轴承故障诊断产生不利的影响,从而降低各种传统诊断方法的效能。为此,本文以滚动轴承故障振动信号为研究对象,将流形学习方法与现代信号处理理论结合,对滚动轴承故障诊断过程中遇到的降噪、特征提取、故障源分离、性能监测问题进行研究。论文的主要研究内容及结论如下：
1.论述了开展滚动轴承故障诊断的意义,分析了滚动轴承故障诊断技术不同发展阶段的特点。对滚动轴承故障诊断中遇到的降噪、特征提取、故障源分离、性能监测问题的研究现状进行了综述。在总结滚动轴承故障诊断技术发展趋势的基础上,介绍了非线性流形学习理论及其在故障诊断中的应用。
2.针对滚动轴承故障信号降噪问题,提出了一种基于最大方差展开(MVU)算法的对偶树复小波(DTCWT)降噪方法。利用MVU提取DTCWT细节信号空间的信号子空间,去除噪声子空间实现降噪。DTCWT具备平移不变性和完全重构性,能克服常规小波变换平移敏感性和非完全重构的缺陷。MVU流形算法能有效提取高维数据空间的非线性结构,克服线性结构的不足。结合二者优势的DTCWT_MVU降噪方法,比传统降噪方法具有更高的信噪比,能更好地提取故障信号的非线性冲击成分,减少降噪后信号波形的失真。仿真和工程实际信号验证了该方法的有效性。
3.对于滚动轴承故障特征提取问题,提出了一种基于张量流形学习的时频故障特征提取方法。在HHT时频特征的基础上,利用张量流形学习方法提取信号的非线性张量流形时频特征,定义了时频特征参数,将张量流形时频特征参数与概率神经网络相结合,准确实现了轴承故障样本分类。张量流形学习能有效提取高维时频特征组合的内蕴非线性特征,与HHT时频特征参数相比,张量流形时频特征参数能减少特征信息的冗余,更有效地区分不同类型故障样本,降低神经网络的迭代次数,提高故障分类的准确性。
4.对于滚动轴承故障源分离问题,提出了一种基于流形学习的滚动轴承故障源盲分离方法。利用EMD分解构造了多通道测试信号,估计测试信号的信源数,建立最优测试信号的选择标准,综合利用峭度、稀疏度、互信息标准选择最优测试信号,通过提取最优测试信号的KPCA流形成分作为ICA算法的输入,有效分离出故障源。该方法解决了欠定盲分离过程中最优测试信号的选取问题,利用流形学习增强了ICA的分离能力,使其能从故障信息微弱的单通道信号中分离出冲击特征明显的故障源。
5.针对滚动轴承性能退化监测问题,提出了一种基于流形学习和模糊聚类的性能监测方法。利用小波包分解确定监测信号的敏感频带,在此基础上提取信号的低维流形特征作为模糊聚类的数据样本,以样本的隶属度值作为性能指标,监测轴承性能退化规律。与基于单特征及线形多特征的监测方法相比,该方法能有效体现滚动轴承全寿命性能退化周期的四个阶段,反映滚动轴承各部件性能退化的统一规律,提前预知轴承早期故障。
6.使用LabVIEW和MATLAB混合编程的方式开发了基于流形学习的滚动轴承故障分析诊断系统。介绍了系统开发的软硬件环境和结构方案,通过实例演示了系统的基本功能,验证了系统的有效性。
Rolling element bearing is the most commonly used key component of machinery. It is an important part of the equipment maintenance work to ensure the safe and stable operation of rolling element bearing. But the working condition of the rolling element bearing is very complex, and its speed varies greatly, the bearing way is sundry, the movement is uncertain. These factors would adversely affect rolling bearing fault diagnosis, thereby reducing the performance of traditional diagnosis methods. In this paper, the rolling element bearing fault signal is regarded as research object. The manifold learning method is combined with other modern signal processing theory to research a series of questions such as noise reduction, feature extraction, fault source separation, performance monitoring in the process of rolling element bearing fault diagnosis. The main research contents and conclusions are as follows:
1. The significance of rolling bearing fault diagnosis is discussed. The features of rolling element bearing fault diagnosis technology in different development phases are analysed. Problems such as noise reduction, feature extraction, fault source separation, performance monitoring are encountered in the rolling element bearing fault diagnosis; and the research status of each issue is reviewed. The nonlinear manifold learning theory and its application in fault diagnosis is introduced on the basis of summing up the fault diagnosis technology trends of rolling element bearing.
2. For rolling element bearing fault signal denoising, the dual tree complex wavelet (DTCWT) denoising method based on maximum variance unfolding (MVU) is proposed. The signal subspace of DTCWT detail signal space is extracted by MVU. The noise reduction is achieved by removing the noise subspace. The defects of conventional wavelet transform such as translational sensitivity and non-perfect reconstruction can be overcomed by the translation invariance and perfect reconstruction of DTCWT. The nonlinear structure of the high-dimensional data space can be effectively extracted by MVU manifold lgorithm; the lack of linear structure is overcomed. The both advantages are combined by DTCWT_MVU denoising method. Not only a higher signal-to-noise ratio can be obtained, but also the nonlinear impact of bearing fault signal can be obtained through DTCWT_MVU denosing method. The denoised waveform distortion is reduced. The effectiveness of this method is vertified by the emulation signal and the engineering signal.
3. For extracting the fault features of rolling element bearings, the time-frequency fault feature extraction method based on tensor manifold learning is proposed. Based on the time-frequency features of HHT, the signals nonlinear tensor manifold time-frequency features are extracted by tensor manifold learning method. The time-frequency feature parameters are defined. The bearing fault samples are accurately classified by combing the tensor manifold time-frequency feature parameters with the probabilistic neural network. The intrinsic nonlinear features of high-dimensional time-frequency features can be effectively extracted by tensor manifold learning method. Compared with HHT time frequency feature parameters, the tensor manifold time-frequency feature parameters can be used to reduce the redundancy of the feature information, to more effectively classify the different types of fault samples, to reduce the iterations of neural network, to improve the fault classification accuracy.
4. To separate the rolling element bearing fault source, the fault source blind separation method of rolling element bearing based on manifold learning is proposed. The multi-channel test signals are structured by EMD decomposition. The number of test signal source is estimated. The optimal test signal selection criteria are established. The best test signal is chosen by comprehensivly utilizing the kurtosis, sparsity, mutual information standard. The fault source is effectively separatated by extracting the KPCA manifold composition as the input to ICA algorithm. The optimal test signal selection in underdetermined blind separation process is solved by this method. The separation ability of ICA is enhanced by using manifold learning, so that the fault source with obvious impact feature can be separated from the single-channel signal with weak fault information.
5. To deal with the performance degradation monitoring of rolling element bearing, the performance monitoring method based on manifold learning and fuzzy clustering is proposed. Sensitive band of the monitoring signal is determined by wavelet packet decomposition. On this basis, the low dimensional manifold features of signal are extracted as data samples of fuzzy clustering. Sample membership can be used as performance indicator to monitor the law of bearing performance degradation. Compared with the monitoring methods based on single feature and linear multi-features, the four stages of rolling element bearing performance degradation in the whole life cycle can be effectively reflected by this method. The uniform performance degradation law of rolling element bearing parts can be reflected. The early bearing fault can be predicted in advance.
6. Bearing fault analysis and diagnosis system based on manifold learning is developed through LabVIEW and MATLAB mixed programming. The system development environment and structure scheme of hardware and software is introduced. The basic functions of the system are demonstrated. The effectiveness of the system is verified by examples.
1.论述了开展滚动轴承故障诊断的意义,分析了滚动轴承故障诊断技术不同发展阶段的特点。对滚动轴承故障诊断中遇到的降噪、特征提取、故障源分离、性能监测问题的研究现状进行了综述。在总结滚动轴承故障诊断技术发展趋势的基础上,介绍了非线性流形学习理论及其在故障诊断中的应用。
2.针对滚动轴承故障信号降噪问题,提出了一种基于最大方差展开(MVU)算法的对偶树复小波(DTCWT)降噪方法。利用MVU提取DTCWT细节信号空间的信号子空间,去除噪声子空间实现降噪。DTCWT具备平移不变性和完全重构性,能克服常规小波变换平移敏感性和非完全重构的缺陷。MVU流形算法能有效提取高维数据空间的非线性结构,克服线性结构的不足。结合二者优势的DTCWT_MVU降噪方法,比传统降噪方法具有更高的信噪比,能更好地提取故障信号的非线性冲击成分,减少降噪后信号波形的失真。仿真和工程实际信号验证了该方法的有效性。
3.对于滚动轴承故障特征提取问题,提出了一种基于张量流形学习的时频故障特征提取方法。在HHT时频特征的基础上,利用张量流形学习方法提取信号的非线性张量流形时频特征,定义了时频特征参数,将张量流形时频特征参数与概率神经网络相结合,准确实现了轴承故障样本分类。张量流形学习能有效提取高维时频特征组合的内蕴非线性特征,与HHT时频特征参数相比,张量流形时频特征参数能减少特征信息的冗余,更有效地区分不同类型故障样本,降低神经网络的迭代次数,提高故障分类的准确性。
4.对于滚动轴承故障源分离问题,提出了一种基于流形学习的滚动轴承故障源盲分离方法。利用EMD分解构造了多通道测试信号,估计测试信号的信源数,建立最优测试信号的选择标准,综合利用峭度、稀疏度、互信息标准选择最优测试信号,通过提取最优测试信号的KPCA流形成分作为ICA算法的输入,有效分离出故障源。该方法解决了欠定盲分离过程中最优测试信号的选取问题,利用流形学习增强了ICA的分离能力,使其能从故障信息微弱的单通道信号中分离出冲击特征明显的故障源。
5.针对滚动轴承性能退化监测问题,提出了一种基于流形学习和模糊聚类的性能监测方法。利用小波包分解确定监测信号的敏感频带,在此基础上提取信号的低维流形特征作为模糊聚类的数据样本,以样本的隶属度值作为性能指标,监测轴承性能退化规律。与基于单特征及线形多特征的监测方法相比,该方法能有效体现滚动轴承全寿命性能退化周期的四个阶段,反映滚动轴承各部件性能退化的统一规律,提前预知轴承早期故障。
6.使用LabVIEW和MATLAB混合编程的方式开发了基于流形学习的滚动轴承故障分析诊断系统。介绍了系统开发的软硬件环境和结构方案,通过实例演示了系统的基本功能,验证了系统的有效性。
Rolling element bearing is the most commonly used key component of machinery. It is an important part of the equipment maintenance work to ensure the safe and stable operation of rolling element bearing. But the working condition of the rolling element bearing is very complex, and its speed varies greatly, the bearing way is sundry, the movement is uncertain. These factors would adversely affect rolling bearing fault diagnosis, thereby reducing the performance of traditional diagnosis methods. In this paper, the rolling element bearing fault signal is regarded as research object. The manifold learning method is combined with other modern signal processing theory to research a series of questions such as noise reduction, feature extraction, fault source separation, performance monitoring in the process of rolling element bearing fault diagnosis. The main research contents and conclusions are as follows:
1. The significance of rolling bearing fault diagnosis is discussed. The features of rolling element bearing fault diagnosis technology in different development phases are analysed. Problems such as noise reduction, feature extraction, fault source separation, performance monitoring are encountered in the rolling element bearing fault diagnosis; and the research status of each issue is reviewed. The nonlinear manifold learning theory and its application in fault diagnosis is introduced on the basis of summing up the fault diagnosis technology trends of rolling element bearing.
2. For rolling element bearing fault signal denoising, the dual tree complex wavelet (DTCWT) denoising method based on maximum variance unfolding (MVU) is proposed. The signal subspace of DTCWT detail signal space is extracted by MVU. The noise reduction is achieved by removing the noise subspace. The defects of conventional wavelet transform such as translational sensitivity and non-perfect reconstruction can be overcomed by the translation invariance and perfect reconstruction of DTCWT. The nonlinear structure of the high-dimensional data space can be effectively extracted by MVU manifold lgorithm; the lack of linear structure is overcomed. The both advantages are combined by DTCWT_MVU denoising method. Not only a higher signal-to-noise ratio can be obtained, but also the nonlinear impact of bearing fault signal can be obtained through DTCWT_MVU denosing method. The denoised waveform distortion is reduced. The effectiveness of this method is vertified by the emulation signal and the engineering signal.
3. For extracting the fault features of rolling element bearings, the time-frequency fault feature extraction method based on tensor manifold learning is proposed. Based on the time-frequency features of HHT, the signals nonlinear tensor manifold time-frequency features are extracted by tensor manifold learning method. The time-frequency feature parameters are defined. The bearing fault samples are accurately classified by combing the tensor manifold time-frequency feature parameters with the probabilistic neural network. The intrinsic nonlinear features of high-dimensional time-frequency features can be effectively extracted by tensor manifold learning method. Compared with HHT time frequency feature parameters, the tensor manifold time-frequency feature parameters can be used to reduce the redundancy of the feature information, to more effectively classify the different types of fault samples, to reduce the iterations of neural network, to improve the fault classification accuracy.
4. To separate the rolling element bearing fault source, the fault source blind separation method of rolling element bearing based on manifold learning is proposed. The multi-channel test signals are structured by EMD decomposition. The number of test signal source is estimated. The optimal test signal selection criteria are established. The best test signal is chosen by comprehensivly utilizing the kurtosis, sparsity, mutual information standard. The fault source is effectively separatated by extracting the KPCA manifold composition as the input to ICA algorithm. The optimal test signal selection in underdetermined blind separation process is solved by this method. The separation ability of ICA is enhanced by using manifold learning, so that the fault source with obvious impact feature can be separated from the single-channel signal with weak fault information.
5. To deal with the performance degradation monitoring of rolling element bearing, the performance monitoring method based on manifold learning and fuzzy clustering is proposed. Sensitive band of the monitoring signal is determined by wavelet packet decomposition. On this basis, the low dimensional manifold features of signal are extracted as data samples of fuzzy clustering. Sample membership can be used as performance indicator to monitor the law of bearing performance degradation. Compared with the monitoring methods based on single feature and linear multi-features, the four stages of rolling element bearing performance degradation in the whole life cycle can be effectively reflected by this method. The uniform performance degradation law of rolling element bearing parts can be reflected. The early bearing fault can be predicted in advance.
6. Bearing fault analysis and diagnosis system based on manifold learning is developed through LabVIEW and MATLAB mixed programming. The system development environment and structure scheme of hardware and software is introduced. The basic functions of the system are demonstrated. The effectiveness of the system is verified by examples.
引文
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