振动谱表征空间滚动轴承寿命状态方法研究
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摘要
空间滚动轴承是空间运动机构的关键零部件，卫星、航天飞机、宇宙飞船等空间飞行器的机械部件能否正常运转、实现预定功能和达到预期寿命，在很大程度上取决于飞行器内各机械部件中滚动轴承的性能、寿命和可靠性。在空间服役的滚动轴承要承受低温、交变温度、高能粒子辐照、原子氧侵蚀、微尘冲刷等极端环境的综合作用，其失效行为和机理与地面常规环境有很大差异。因此，为了满足空间飞行器高可靠、长寿命的发展需求，亟需在模拟空间环境下开展滚动轴承寿命评估和预测等方面的研究，而研究基础在于寿命状态的描述和识别，即寿命状态的表征。目前，摩擦力矩、温度等参数等不能有效反映空间滚动轴承寿命状态的变化，一个新的思路是以包含运行状态信息丰富的振动信号为切入点研究空间滚动轴承轴承寿命状态的表征方法。当空间滚动轴承的寿命状态发生变化时，轴承振动信号时域中的幅值和概率分布将会发生变化，频域中的频率成分、不同频率成分的能量，以及频谱的主能量谱峰位置也将发生改变。本文以空间滚动轴承为研究对象，开展其寿命状态的振动谱表征方法研究。主要研究内容如下：
①针对空间滚动轴承寿命状态的表征问题，提出了时频域特征参数构建空间滚动轴承寿命状态特征向量的方法。空间滚动轴承处于不同的磨损程度即处于不同寿命状态时，其振动情况发生变化，直接表现为振动信号特征的改变。通过振动信号时域波形和频谱特征来反映空间滚动轴承振动信号的时域和频域信息，从而指示不同寿命状态之间的差异性。为了便于对寿命状态的自动识别并全面准确的反映空间滚动轴承的寿命状态，综合利用时域和频域特征参数，选择了16个时域特征参数和14个频域特征参数构造出高维混合域特征向量即空间滚动轴承寿命状态特征向量作为轴承寿命状态的振动谱特征。同时，分析了转速和载荷对空间滚动轴承寿命和振动的影响，提出了空间滚动轴承全寿命试验方法与策略；
②针对空间滚动轴承振动信号降噪和背景噪声滤除问题，提出了基于集合经验模式分解（Ensemble Empirical Mode Decomposition，EEMD）的降噪和干扰滤除方法。在模拟真空环境下测试得到的滚动轴承振动信号往往受到测试系统噪声和模拟真空环境设备运行而带入的背景噪声的干扰，为了提高信噪比，准确提取空间轴承寿命状态特征向量，需要将上述两类噪声进行降噪和滤除。对于空间滚动轴承振动信号的降噪，应用EEMD能有效抑制模式混叠的特性，根据白噪声经EEMD分解后其固有模式分量（Intrinsic Mode Functions，IMF）的特性设计了一种自动选取IMF分量重构信号的算法，实现了信号自适应降噪。对于背景噪声的滤除，根据EEMD的滤波特性，通过计算含背景噪声的轴承振动信号的IMF分量和单独测试得到的背景噪声的IMF分量之间的相关系数对IMF分量进行筛选并重构信号，从而将由于模拟真空环境而带入的背景噪声进行了有效滤除。同时，针对EEMD的两个重要参数，即加入白噪声的幅值系数k和总体平均次数M的选取问题，根据不同幅值系数的白噪声对信号极值点分布均匀性影响规律，提出了EEMD自适应参数优化方法，保证了分解精度和计算效率；
③针对空间滚动轴承寿命状态识别问题，提出了基于流形学习的空间滚动轴承寿命状态识别方法。轴承振动信号往往呈现出特征信息耦合、时变性强的特点，由其时频域特征参数构成的高维寿命状态特征向量内部的必然存在信息冗余和相互耦合，不利于进行分类识别。为此，利用流形学习方法对寿命状态特征向量进行约简，提取出原始观测空间的真实流形结构，最终得到维数低、敏感性高和分类错误率小的主要特征向量。然后，将经过约简后的训练样本和测试样本的低维寿命状态特征向量输入最近邻分类器（K-Nearest Neighbors Classifier，KNNC），最近邻分类器根据训练样本的邻域信息和类标签信息对测试样本进行分类决策，实现寿命状态的识别。
④根据空间滚动轴承振动特点及寿命试验要求，搭建了测试系统硬件平台，开发了集信号自动采集、处理和寿命状态识别等功能为一体的空间滚动轴承寿命状态识别系统。系统不仅可对大量数据文件进行批量管理、分析，还应用数据库技术实现寿命样本库的建立、添加和删除等管理，保证了寿命样本的完整性和安全性。系统主要包括：振动信号采集模块，振动信号基本分析模块（包括波形分析、概率分析、相关分析和自谱分析等）、振动谱瀑布图分析模块、HHT分析模块和寿命状态识别模块。最后，通过对空间滚动轴承寿命状态识别应用实例检验了该系统的可行性和有效性。
文章最后对本文的工作进行了总结，并展望了下一步的研究方向。
The space rolling bearing is the key components in the space motion mechanism,the spacecraft such as satellite, space shuttle and spaceship, can run normally toachieve a predetermined function and achieve the life expectancy,which largelydepends on the performance,lifetime and reliability of rolling bearing in themechanical components of spacecraft. The rolling bearing service in space is subjectedto comprehensively effect by extreme environments such as low temperature andalternating temperature, high-energy particle radiation, atomic oxygen erosion, dustand erosion. So, there are great differences in failure behavior and mechanism withconventional environment. Therefore, in order to meet development needs of the highreliability and long-life of spacecraft, life evaluation and prediction for space rollingbearing are necessitated to study in the vacuum, and the research foundation is themethods of description and identification of the lifetime state for space rollingbearings,namely the lifetime state indentification for sapace rolling bearing. At present,the friction torque and temperature cann’t effectively reflect the changes in the state ofspace rolling bearing lifetime, and a new thought is obtained,which study the methodsof characterize lifetime state for space rolling bearings based on vibration. While thespace rolling bearing lifetime state changed, the amplitude of the bearing vibrationsignal in the time domain and the probability distribution would be changed, and thefrequency component, the energy of different frequency components, and the mainenergy peak position of the spectrum also would be changed. According to theproblems in characterization and identification for lifetime state of space rollingbearing, the main research work and conclusions are as follows:
①For the issue of the characterization of the space rolling bearing lifetime state, themechanism of characterizing lifetime state for space rolling bearing is proposed. Thedifferent lifetime state and the degree of wear of the space rolling bearing is reflectedby the change of vibration signal characteristics. It reflects the space rolling bearingoperation state through the vibration signal features extracted in the time waveformand frequency spectrum. Therefore, the difference among different lifetime state can beindicated clearly. Finally, the accurately characterization and identification for thelifetime status of space rolling bearing is obtained. In order to automatic identify andfully reflect the lifetime state of space rolling bearings, comprehensive utilization of time domain and frequency domain characteristic parameters,16characteristicparameters of the time-domain and14frequency domain characteristic parameters areselected to construct the high-dimensional mixed-domain feature vector ascharacteristics of vibration spectrum for the space rolling bearing. Then, the lifetimestate eigenvectors of space rolling bearing is obtained. At the same time, the effect ofspeed and load for the space rolling bearing lifetime is analysed, and the vibration ofthe space rolling bearing is also analysed in this researched. Then, the methods andstrategies of complete life test for space rolling bearing is proposed;
②According to the problems of de-noising and background noise filtering for thespace rolling bearing, the de-noising and filtering methods based on the ensembleempirical mode decomposition(EEMD) for the space rolling bearing are proposed. Thevibration signals of rolling bearing are obtained in the simulation vacuum environment.However they are disturbed by the noise and background noise, amonge which fromtest system and the background noise is from the equipment which maintain analogvacuum environment.In order to increase signal to noise ratio, accurately extract thelifetime state eigenvectors of space rolling bearing, two types of noise should bede-noised and filtered. For vibration signal de-noising of space rolling bearing, theensemble empirical mode decomposition (EEMD) can effectively suppress thephenomenon of mode mixing, and according to the product of the energy density ofintrinsic mode functions (IMFs) from the white noise by EMD and the correspondingaveraged period of IMFs is a constant, an automatic algorithm of choose IMFcomponents to reconstruct signal is designed, and an adaptive de-noising method basedon EEMD for vibration signal is proposed. For background noise filtered, according tothe filtering characteristics of EEMD, calculating the correlation coefficient betweenthe IMF component of the background noise and the IMF component of the vibrationsignal which includes background noise, then, the IMF component is selected based onthe correlation coefficient.At last, the background noise which come from the vacuumenvironment is effectively filtered out. Aim to the selection for two importantparameters, the amplitude coefficient k of the white noise and the number of ensembletrials, M, the adaptive selection methods for EEMD parameters is proposed, based onthe influence law of distribution uniformity on the signal extreme point because ofdifferent amplitude coefficient of wihite noise.In the end,the decomposition accuracyand computational efficiencys is ensured.
③Aim to the problems of identification of lifetime state for space rolling bearing, the methods of lifetime state identification based on manifold learing is proposed. Thefeatures of imformation couping and time-varying are presented in the vibration signalof bearing, then, the features are presented in the high-dimensional lifetime stateeigenvectors, which go against classification and identification. Thus, thehigh-dimensional lifetime state eigenvectors is compressed based on the excellentperformance characteristics of compression and classification from manifold learning,the real manifold is extracted in the original observation spatial, and the goodclassification characteristics,high sensitivity and low-dimensional life stateeigenvectors is obtained.Then,the low-dimensional feature vector of training samplesand testing samples is inputed into the K-nearest neighbors classifier(KNNC), and thetesting samples is made classification decision of KNNC based on the neighborhoodand the class label information of training samples, at last,the lifetime state for spacerolling bearing is identified.
④On the basis of research of this paper, the hardware platform of test system isbuilded based on the vibration characteristics and lifetime testing requirements forspace rolling bearing.The lifetime state identification system of space rolling bearing isimplemented, including signal acquisition signal, processing, lifetime stateidentification and others functions. This system can be used not only for managementof large data set, but also can be management of lifetime state sample database basedon database technology, and the integrity and security of lifetime state sample databaseis ensured. The system mainly includes: vibration signal acquisition module, vibrationsignal analysis module (including waveform analysis, probability analysis, correlationanalysis and spectrum analysis, vibration spectrum), waterfall analysis module, HHTanalysis module and life state identification module. At last, the lifetime state of spacerolling bearing is exact identified using this system and the result shows that thesystem is feasible and valid.
①针对空间滚动轴承寿命状态的表征问题，提出了时频域特征参数构建空间滚动轴承寿命状态特征向量的方法。空间滚动轴承处于不同的磨损程度即处于不同寿命状态时，其振动情况发生变化，直接表现为振动信号特征的改变。通过振动信号时域波形和频谱特征来反映空间滚动轴承振动信号的时域和频域信息，从而指示不同寿命状态之间的差异性。为了便于对寿命状态的自动识别并全面准确的反映空间滚动轴承的寿命状态，综合利用时域和频域特征参数，选择了16个时域特征参数和14个频域特征参数构造出高维混合域特征向量即空间滚动轴承寿命状态特征向量作为轴承寿命状态的振动谱特征。同时，分析了转速和载荷对空间滚动轴承寿命和振动的影响，提出了空间滚动轴承全寿命试验方法与策略；
②针对空间滚动轴承振动信号降噪和背景噪声滤除问题，提出了基于集合经验模式分解（Ensemble Empirical Mode Decomposition，EEMD）的降噪和干扰滤除方法。在模拟真空环境下测试得到的滚动轴承振动信号往往受到测试系统噪声和模拟真空环境设备运行而带入的背景噪声的干扰，为了提高信噪比，准确提取空间轴承寿命状态特征向量，需要将上述两类噪声进行降噪和滤除。对于空间滚动轴承振动信号的降噪，应用EEMD能有效抑制模式混叠的特性，根据白噪声经EEMD分解后其固有模式分量（Intrinsic Mode Functions，IMF）的特性设计了一种自动选取IMF分量重构信号的算法，实现了信号自适应降噪。对于背景噪声的滤除，根据EEMD的滤波特性，通过计算含背景噪声的轴承振动信号的IMF分量和单独测试得到的背景噪声的IMF分量之间的相关系数对IMF分量进行筛选并重构信号，从而将由于模拟真空环境而带入的背景噪声进行了有效滤除。同时，针对EEMD的两个重要参数，即加入白噪声的幅值系数k和总体平均次数M的选取问题，根据不同幅值系数的白噪声对信号极值点分布均匀性影响规律，提出了EEMD自适应参数优化方法，保证了分解精度和计算效率；
③针对空间滚动轴承寿命状态识别问题，提出了基于流形学习的空间滚动轴承寿命状态识别方法。轴承振动信号往往呈现出特征信息耦合、时变性强的特点，由其时频域特征参数构成的高维寿命状态特征向量内部的必然存在信息冗余和相互耦合，不利于进行分类识别。为此，利用流形学习方法对寿命状态特征向量进行约简，提取出原始观测空间的真实流形结构，最终得到维数低、敏感性高和分类错误率小的主要特征向量。然后，将经过约简后的训练样本和测试样本的低维寿命状态特征向量输入最近邻分类器（K-Nearest Neighbors Classifier，KNNC），最近邻分类器根据训练样本的邻域信息和类标签信息对测试样本进行分类决策，实现寿命状态的识别。
④根据空间滚动轴承振动特点及寿命试验要求，搭建了测试系统硬件平台，开发了集信号自动采集、处理和寿命状态识别等功能为一体的空间滚动轴承寿命状态识别系统。系统不仅可对大量数据文件进行批量管理、分析，还应用数据库技术实现寿命样本库的建立、添加和删除等管理，保证了寿命样本的完整性和安全性。系统主要包括：振动信号采集模块，振动信号基本分析模块（包括波形分析、概率分析、相关分析和自谱分析等）、振动谱瀑布图分析模块、HHT分析模块和寿命状态识别模块。最后，通过对空间滚动轴承寿命状态识别应用实例检验了该系统的可行性和有效性。
文章最后对本文的工作进行了总结，并展望了下一步的研究方向。
The space rolling bearing is the key components in the space motion mechanism,the spacecraft such as satellite, space shuttle and spaceship, can run normally toachieve a predetermined function and achieve the life expectancy,which largelydepends on the performance,lifetime and reliability of rolling bearing in themechanical components of spacecraft. The rolling bearing service in space is subjectedto comprehensively effect by extreme environments such as low temperature andalternating temperature, high-energy particle radiation, atomic oxygen erosion, dustand erosion. So, there are great differences in failure behavior and mechanism withconventional environment. Therefore, in order to meet development needs of the highreliability and long-life of spacecraft, life evaluation and prediction for space rollingbearing are necessitated to study in the vacuum, and the research foundation is themethods of description and identification of the lifetime state for space rollingbearings,namely the lifetime state indentification for sapace rolling bearing. At present,the friction torque and temperature cann’t effectively reflect the changes in the state ofspace rolling bearing lifetime, and a new thought is obtained,which study the methodsof characterize lifetime state for space rolling bearings based on vibration. While thespace rolling bearing lifetime state changed, the amplitude of the bearing vibrationsignal in the time domain and the probability distribution would be changed, and thefrequency component, the energy of different frequency components, and the mainenergy peak position of the spectrum also would be changed. According to theproblems in characterization and identification for lifetime state of space rollingbearing, the main research work and conclusions are as follows:
①For the issue of the characterization of the space rolling bearing lifetime state, themechanism of characterizing lifetime state for space rolling bearing is proposed. Thedifferent lifetime state and the degree of wear of the space rolling bearing is reflectedby the change of vibration signal characteristics. It reflects the space rolling bearingoperation state through the vibration signal features extracted in the time waveformand frequency spectrum. Therefore, the difference among different lifetime state can beindicated clearly. Finally, the accurately characterization and identification for thelifetime status of space rolling bearing is obtained. In order to automatic identify andfully reflect the lifetime state of space rolling bearings, comprehensive utilization of time domain and frequency domain characteristic parameters,16characteristicparameters of the time-domain and14frequency domain characteristic parameters areselected to construct the high-dimensional mixed-domain feature vector ascharacteristics of vibration spectrum for the space rolling bearing. Then, the lifetimestate eigenvectors of space rolling bearing is obtained. At the same time, the effect ofspeed and load for the space rolling bearing lifetime is analysed, and the vibration ofthe space rolling bearing is also analysed in this researched. Then, the methods andstrategies of complete life test for space rolling bearing is proposed;
②According to the problems of de-noising and background noise filtering for thespace rolling bearing, the de-noising and filtering methods based on the ensembleempirical mode decomposition(EEMD) for the space rolling bearing are proposed. Thevibration signals of rolling bearing are obtained in the simulation vacuum environment.However they are disturbed by the noise and background noise, amonge which fromtest system and the background noise is from the equipment which maintain analogvacuum environment.In order to increase signal to noise ratio, accurately extract thelifetime state eigenvectors of space rolling bearing, two types of noise should bede-noised and filtered. For vibration signal de-noising of space rolling bearing, theensemble empirical mode decomposition (EEMD) can effectively suppress thephenomenon of mode mixing, and according to the product of the energy density ofintrinsic mode functions (IMFs) from the white noise by EMD and the correspondingaveraged period of IMFs is a constant, an automatic algorithm of choose IMFcomponents to reconstruct signal is designed, and an adaptive de-noising method basedon EEMD for vibration signal is proposed. For background noise filtered, according tothe filtering characteristics of EEMD, calculating the correlation coefficient betweenthe IMF component of the background noise and the IMF component of the vibrationsignal which includes background noise, then, the IMF component is selected based onthe correlation coefficient.At last, the background noise which come from the vacuumenvironment is effectively filtered out. Aim to the selection for two importantparameters, the amplitude coefficient k of the white noise and the number of ensembletrials, M, the adaptive selection methods for EEMD parameters is proposed, based onthe influence law of distribution uniformity on the signal extreme point because ofdifferent amplitude coefficient of wihite noise.In the end,the decomposition accuracyand computational efficiencys is ensured.
③Aim to the problems of identification of lifetime state for space rolling bearing, the methods of lifetime state identification based on manifold learing is proposed. Thefeatures of imformation couping and time-varying are presented in the vibration signalof bearing, then, the features are presented in the high-dimensional lifetime stateeigenvectors, which go against classification and identification. Thus, thehigh-dimensional lifetime state eigenvectors is compressed based on the excellentperformance characteristics of compression and classification from manifold learning,the real manifold is extracted in the original observation spatial, and the goodclassification characteristics,high sensitivity and low-dimensional life stateeigenvectors is obtained.Then,the low-dimensional feature vector of training samplesand testing samples is inputed into the K-nearest neighbors classifier(KNNC), and thetesting samples is made classification decision of KNNC based on the neighborhoodand the class label information of training samples, at last,the lifetime state for spacerolling bearing is identified.
④On the basis of research of this paper, the hardware platform of test system isbuilded based on the vibration characteristics and lifetime testing requirements forspace rolling bearing.The lifetime state identification system of space rolling bearing isimplemented, including signal acquisition signal, processing, lifetime stateidentification and others functions. This system can be used not only for managementof large data set, but also can be management of lifetime state sample database basedon database technology, and the integrity and security of lifetime state sample databaseis ensured. The system mainly includes: vibration signal acquisition module, vibrationsignal analysis module (including waveform analysis, probability analysis, correlationanalysis and spectrum analysis, vibration spectrum), waterfall analysis module, HHTanalysis module and life state identification module. At last, the lifetime state of spacerolling bearing is exact identified using this system and the result shows that thesystem is feasible and valid.
引文
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