基于优化支持向量机的空间滚动轴承寿命预测方法研究
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摘要
空间滚动轴承高可靠与长寿命研究，是支撑高可靠、长寿命空间飞行器发展的基础保障性研究内容之一，而空间滚动轴承寿命评估和预测是空间滚动轴承高可靠与长寿命研究的重要内容。空间滚动轴承要承受低温和交变温度、高能粒子辐照、原子氧侵蚀、碎片撞击及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，尚缺乏深入的规律性认识。目前只能通过地面模拟空间环境开展加速寿命试验，获取失效数据，进行空间滚动轴承寿命评估和预测研究。由于空间滚动轴承摩擦力矩不能有效反映其寿命特征，论文选用包涵空间滚动轴承寿命特征信息丰富的振动信号为研究对象，通过研究空间滚动轴承精度失效导致性能退化的激振机理，揭示空间滚动轴承振动特征随退化趋势的演变规律；通过对空间滚动轴承振动信号的处理，提取空间滚动轴承衰退性能指标；通过构建人工智能模型，实现空间滚动轴承寿命预测。对于指导空间滚动轴承的设计与制造、建立使用寿命的评估准则、提高后续空间飞行器的在轨寿命和使用性能都具有重要的意义。论文主要的研究内容如下：
①针对极端环境下空间滚动轴承失效机理复杂、失效特征难以表征的问题，研究空间滚动轴承精度失效导致性能退化的激振机理，建立空间滚动轴承振动信号和退化性能之间的联系，揭示空间滚动轴承振动特征随退化趋势的演变规律，为后期通过空间滚动轴承振动数据预测轴承的退化趋势和剩余寿命奠定了理论基础。
②针对地面模拟空间环境实验中空间滚动轴承振动信号背景噪声较为严重的问题，研究利用盲源分离算法进行背景噪声消除的滤波方法。分析地面模拟空间环境通过加速寿命试验采集到的轴承振动信号的背景噪声成分及其特征，研究背景噪声虚拟信号的构建方法，构建盲源分离模型消除背景噪声。应用结果表明在不影响有效特征信息的情况下能够有效剔除背景噪声的干扰。实现了以50Hz及其倍频成分的噪声信息的消除。
③针对空间滚动轴承寿命退化趋势指标难以构建的问题，研究基于空间特征信息加权融合的衰退性能指标构建方法。研究时域、频域和时频域的特征信息的提取方法，探索这些多维特征信息的空间分布，构建基于数据空间映射及加权融合的特征指标，研究结果表明所建立的衰退性能指标能够较好地反映轴承的退化趋势。从而实现了基于第一主分量的衰退性能指标的建立。
④针对传统的寿命预测方法不能有效预测空间滚动轴承寿命的问题，研究基于优化支持向量机的空间滚动轴承寿命预测方法。采用相空间重构进行支持向量机输入参数的选取，利用粒子群算法进行支持向量机内部参数优化，建立基于优化参数的退化趋势预测模型，实现空间滚动轴承退化趋势和剩余寿命的准确预测，通过不同方法的对比表明本文所提的方法的预测效果优于相关文献的预测效果。
⑤研发空间滚动轴承性能退化趋势预测模块、剩余寿命预测模块、人机交互等功能模块，实现空间滚动轴承寿命预测的功能。
文章最后对本文的工作进行总结，并展望下一步的研究方向。
The long life and high reliable research of the space bearing is one of the basis forthe development of long-life spacecraft. The life assessment and prediction research ofthe space bearing is the most importment content of the space bearing long life and highreliable research. The space bearing needs to bear alternating temperature, high-energyparticle irradiation, the combined effects of the atomic oxygen erosion, debris impactand dust erosion and other extreme environments, the failure behavior and mechanismis very different from the bearing working in the conventional terrestrial environment,however, there lack of depth understanding of the laws. Currently, we just only throughthe accelerated life testing group in the simulated space environment to get the failuredata, in order to do the space bearing life assessment and prediction research. The paperchosen the vibration signal which indulgences the rich life evolution information towork as the object of the study, due to the friction torque can not effectively reflect thespace bearing life evolution characteristics. By researching the failure mechanism thatleading to the space bearing performance degradation accuracy, this research reveals thevibration characteristics can reflect the space bearing degradation trends. By processingthe space bearing vibration signal, this research extracts the index to reflect the spacebearing degradation process. By building the artificial intelligence model, this researchachieves the space bearing life prediction. All of these researchs will have greatsignificance for guiding the design and manufacture of space bearing, establishing thelife assessment criterion, improve the spacecraft performance subsequently. Thisresearch contents are as follows:
①Because the failure mechanism of the space bearing is very complex under theextreme environment, and the failure characteristics is difficult to reflect the bearingdegradation process, this article researchs the failure mechanism that leading to thespace bearing performance degradation accuracy, establishs the line between thevibration characteristics and the space bearing degradation trends. Reveals that thevibration characteristics changed with the bearing degradation process, this will laid thetheoretical foundation for using the vibration signal to predict the space bearing life.
②Aiming at the problem that the space vibration signal collected from thesimulated space environment experiments contains serious background noise. Thisresearch uses the blind source separation method to eliminate the background noise. Through researchs the components and characteristics of the background noise,constructs the virtual signals and builds the blind source separation model to eliminatethe noise. The application results showed that the method can eliminate the backgroundnoise effectively.
③Aiming at the problem that the space bearing degradation process indicator isvery difficult to been built. This paper constructs the indicator based on the principalcomponent analysis weighted fusion method. Researchs the time domain, frequencydomain and time-frequency domain feature information extraction methods, exploresthe spatial distribution of these features. Constructs the indicator based on the dataspace mapping and weight fusion method, the results show that the constructedindicator can reflect the bearing degradation trend effectively.
④Aiming at the traditional life prediction method can not predict the spacebearing life effectively, this article uses the optimizatic SVM to predict the spacebearing life. Uses the phase space reconstruction method to select the inpur parametersof the SVM, uses the particle swaram algorithm to select the SVM internal parameters.Through the optimized SVM model to achieve the space bearing life prediction, resultsproved the effectiveness of the proposed method.
⑤This research make the space rolling bearing performance degradation trendforecasting soft module and space rolling bearing residual life prediction soft module,human-computer interaction module, so as to meet the need of space bearing lifeprediction function.
Finally, this work is summarized.
①针对极端环境下空间滚动轴承失效机理复杂、失效特征难以表征的问题，研究空间滚动轴承精度失效导致性能退化的激振机理，建立空间滚动轴承振动信号和退化性能之间的联系，揭示空间滚动轴承振动特征随退化趋势的演变规律，为后期通过空间滚动轴承振动数据预测轴承的退化趋势和剩余寿命奠定了理论基础。
②针对地面模拟空间环境实验中空间滚动轴承振动信号背景噪声较为严重的问题，研究利用盲源分离算法进行背景噪声消除的滤波方法。分析地面模拟空间环境通过加速寿命试验采集到的轴承振动信号的背景噪声成分及其特征，研究背景噪声虚拟信号的构建方法，构建盲源分离模型消除背景噪声。应用结果表明在不影响有效特征信息的情况下能够有效剔除背景噪声的干扰。实现了以50Hz及其倍频成分的噪声信息的消除。
③针对空间滚动轴承寿命退化趋势指标难以构建的问题，研究基于空间特征信息加权融合的衰退性能指标构建方法。研究时域、频域和时频域的特征信息的提取方法，探索这些多维特征信息的空间分布，构建基于数据空间映射及加权融合的特征指标，研究结果表明所建立的衰退性能指标能够较好地反映轴承的退化趋势。从而实现了基于第一主分量的衰退性能指标的建立。
④针对传统的寿命预测方法不能有效预测空间滚动轴承寿命的问题，研究基于优化支持向量机的空间滚动轴承寿命预测方法。采用相空间重构进行支持向量机输入参数的选取，利用粒子群算法进行支持向量机内部参数优化，建立基于优化参数的退化趋势预测模型，实现空间滚动轴承退化趋势和剩余寿命的准确预测，通过不同方法的对比表明本文所提的方法的预测效果优于相关文献的预测效果。
⑤研发空间滚动轴承性能退化趋势预测模块、剩余寿命预测模块、人机交互等功能模块，实现空间滚动轴承寿命预测的功能。
文章最后对本文的工作进行总结，并展望下一步的研究方向。
The long life and high reliable research of the space bearing is one of the basis forthe development of long-life spacecraft. The life assessment and prediction research ofthe space bearing is the most importment content of the space bearing long life and highreliable research. The space bearing needs to bear alternating temperature, high-energyparticle irradiation, the combined effects of the atomic oxygen erosion, debris impactand dust erosion and other extreme environments, the failure behavior and mechanismis very different from the bearing working in the conventional terrestrial environment,however, there lack of depth understanding of the laws. Currently, we just only throughthe accelerated life testing group in the simulated space environment to get the failuredata, in order to do the space bearing life assessment and prediction research. The paperchosen the vibration signal which indulgences the rich life evolution information towork as the object of the study, due to the friction torque can not effectively reflect thespace bearing life evolution characteristics. By researching the failure mechanism thatleading to the space bearing performance degradation accuracy, this research reveals thevibration characteristics can reflect the space bearing degradation trends. By processingthe space bearing vibration signal, this research extracts the index to reflect the spacebearing degradation process. By building the artificial intelligence model, this researchachieves the space bearing life prediction. All of these researchs will have greatsignificance for guiding the design and manufacture of space bearing, establishing thelife assessment criterion, improve the spacecraft performance subsequently. Thisresearch contents are as follows:
①Because the failure mechanism of the space bearing is very complex under theextreme environment, and the failure characteristics is difficult to reflect the bearingdegradation process, this article researchs the failure mechanism that leading to thespace bearing performance degradation accuracy, establishs the line between thevibration characteristics and the space bearing degradation trends. Reveals that thevibration characteristics changed with the bearing degradation process, this will laid thetheoretical foundation for using the vibration signal to predict the space bearing life.
②Aiming at the problem that the space vibration signal collected from thesimulated space environment experiments contains serious background noise. Thisresearch uses the blind source separation method to eliminate the background noise. Through researchs the components and characteristics of the background noise,constructs the virtual signals and builds the blind source separation model to eliminatethe noise. The application results showed that the method can eliminate the backgroundnoise effectively.
③Aiming at the problem that the space bearing degradation process indicator isvery difficult to been built. This paper constructs the indicator based on the principalcomponent analysis weighted fusion method. Researchs the time domain, frequencydomain and time-frequency domain feature information extraction methods, exploresthe spatial distribution of these features. Constructs the indicator based on the dataspace mapping and weight fusion method, the results show that the constructedindicator can reflect the bearing degradation trend effectively.
④Aiming at the traditional life prediction method can not predict the spacebearing life effectively, this article uses the optimizatic SVM to predict the spacebearing life. Uses the phase space reconstruction method to select the inpur parametersof the SVM, uses the particle swaram algorithm to select the SVM internal parameters.Through the optimized SVM model to achieve the space bearing life prediction, resultsproved the effectiveness of the proposed method.
⑤This research make the space rolling bearing performance degradation trendforecasting soft module and space rolling bearing residual life prediction soft module,human-computer interaction module, so as to meet the need of space bearing lifeprediction function.
Finally, this work is summarized.
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