机械动力传动系统核基故障识别与状态预测技术研究
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摘要
大型机械动力传动系统作为国防和国民经济领域广泛应用的一类重要技术装备,其安全性可靠性至关重要。由于这类装备结构复杂,耦合比较严重,运行状态具有较强非线性特征,且运行环境恶劣,受非高斯噪声和各种不确定因素影响,对其进行准确的故障诊断困难较大。监控诊断和状态预测作为设备安全可靠运行和科学合理维修的一项重要支持技术,其研究意义重大。
论文从机械动力传动系统运行状态特点和非线性状态监控诊断需求分析出发,结合相关学科领域最新研究成果,利用核方法在处理非线性问题中所具有的特质,系统深入地开展了核基特征提取、核基故障分类与决策、核基状态趋势预测等技术研究,旨在探索提高非线性条件下故障识别正确性、状态预测准确性的技术方法与途径,为机械动力传动系统监控诊断技术向更深层次发展和应用提供有效的技术支持。论文的主要研究内容包括:
1、针对机械动力传动系统故障诊断对特征提取有效性要求,重点研究基于核方法的非线性故障特征提取技术。提出基于核主成分分析的铁谱磨粒特征提取方法,较好地解决了铁谱磨粒参数非线性特征提取和磨粒特征维数压缩问题;提出基于核独立分量分析的直升机传动系统故障特征预处理技术,为提高直升机传动系统早期故障特征提取的有效性奠定了基础;提出基于核Fisher判别分析的特征提取技术,有效增强故障特征的分类特性和提高故障分类识别准确性;
2、针对大型机械动力传动系统存在的故障样本不足或称小样本故障诊断问题,深入研究了支持向量机故障诊断方法。从提高“一对一”、“一对多”两种多分类器算法的训练和分类速度考虑,研究了算法的改进方案;为解决故障诊断中因故障决策错误所造成损失和风险不同的问题,对标准SVM算法加以改进,提出了基于模糊隶属度函数的SVM等风险故障分类决策模型;在上述研究的基础上,结合机械动力传动系统在线故障诊断要求,提出了核基特征提取与SVM多故障层次分类检测模型,通过轴承故障检测与分类应用实例,对模型可行性、有效性进行了检验;
3、针对大型机械动力传动系统缺乏故障样本条件下的故障检测问题,提出基于支持向量数据描述的故障检测方法,深入研究了该方法用于故障检测时核函数的选择及其核函数参数对决策边界区域和检测精度的影响,提出了考虑误判损失不等条件下SVDD决策模型的改进方案。相关方法在某型直升机传动系统轴承故障检测中得到成功应用;
4、针对动力装置运行状态非线性特点和状态预测要求,提出了基于相空间重构和支持向量回归的动力装置状态趋势预测方法,其中,采用相空间重构技术对刻画机组状态及其变化的非线性时间序列进行分析,计算出最小嵌入维作为预测样本向量的维数,利用支持向量回归机对机组运行状态样本学习训练,得到机组状态预测模型,对模型的状态预测能力进行了深入研究。该方法在某型舰船动力装置主汽轮机的运行状态预测中得到成功应用,为大型复杂机械动力传动系统非线性状态预测和掌握运行状态变化趋势提供了有效的解决方案。
The large-scale mechanical power and transmission systems are the types of important technical equipment being used extensively in national defense and national economy realms; their safety and reliability are crucial. Because of a series of characteristics including complicated structures, serious states coupling among the different parts, a strong nonlinear on their condition behaviors, an abominable operational environments and being subject to the non-gauss noise and various indetermination factors, it is quite difficult to carry out the missions of the fault diagnosis accurately for these systems. As an important supporting technology of ensuring that the machines operate safely and reliably and their maintenance actives are organized by the scientific and reasonable manners, researches on the advanced techniques including condition monitoring, fault diagnosis and condition forecasting are of quite significance.
Based on analyzing the nonlinear characteristics and aiming at the technical requirements of nonlinear fault diagnosis and also combining the recently achievements deriving from related academic realms, the kernel-based diagnostic techniques including feature extraction, fault detection/recognition and decision-making, the operational condition forecasting for mechanical power and transmission systems are studied systemically and detailed. The main purpose of the research work is to investigate the new approaches to deal with the problems of fault identification and condition forecasting accurately for the systems. At the same time, the research work also aims at providing valid technical support for promoting the development and application of the fault diagnostic technology. The detailed contents and innovative works are shown in the following sections.
1. To meet the requirement of extracting the effective features for the fault diagnosis of the mechanical power and transmission systems, the kernel-based methods of nonlinear feature extraction are studied. Firstly, the method of the debris feature extraction based on KPCA is presented. It is mainly used to analyze the nonlinear related features and reduce the dimension of the debris parameters. Secondly, the preprocessing technique for the fault feature extraction based on KICA being used in the helicopter transmission system is put forward. The research result demonstrates that the KICA is an effective approach of extracting the features that reflect the incipient faults. Thirdly, the KFDA based method of fault feature extraction is presented. It is good at enhancing the performance of classification for the fault features and promoting the veracity of the fault recognition.
2. To solve the diagnostic problem based on a small quantity of fault sample existed in large-scale mechanical power and transmission systems, the SVM based method of the fault recognition and decision-making is studied in detail. To promote the performances of two schemes of multi-classifier combination including 'one against one' and 'one against rest', the improved methods are proposed respectively, which is good at the training efficiency and can keep the original precision in classification. Considering that the different mistakes of decision-making may lead to unequal loss and risk in fault diagnosis process for the mechanical power and transmission system, the equal risk SVM model based on fuzzy membership function for fault diagnosis is presented. To meet the requirements of online diagnosis for mechanical system, the SVM based multi-layer model for fault detection is studied. Its validity is confirmed by fault detection experiment on bearing fault detection and classification.
3. For the purpose of fault detection for the large-scale mechanical power and transmission systems under the condition of the lack of fault samples, a novel fault detection method based on the SVDD model is put forward. The influences on decision-making boundary and classification precision by choosing different kernel functions and the kernel parameters are deeply studied and analyzed. An improved SVDD based model for fault detection is presented and discussed, which considered unequal loss owing to two kinds of wrong decision-making. The presented method is used for fault detection of the bearings mounted in a helicopter's transmission system and its validity is proved by the experimental studies.
4. Aiming at nonlinear characteristics existed in running condition of the mechanical power devices and combining the support vector regression with phase space reconstruction theory, a novel condition forecasting method is put forward. It is mainly by means of analyzing the nonlinear time series depicted the running condition changes and calculating the minimize embedded dimension which is used for determining the dimension of the sample's vectors by using the phase space reconstruction technology. The model of support vector regression is employed to forecast the running condition and its change trend. The forecasting capability of the model is investigated in detail. The related techniques have been applied successfully in the running condition forecasting of the mechanical power device in a warship. The research results indicate that the presented methods can provide a valid solution for the large scale and complicated machines' condition forecasting.
论文从机械动力传动系统运行状态特点和非线性状态监控诊断需求分析出发,结合相关学科领域最新研究成果,利用核方法在处理非线性问题中所具有的特质,系统深入地开展了核基特征提取、核基故障分类与决策、核基状态趋势预测等技术研究,旨在探索提高非线性条件下故障识别正确性、状态预测准确性的技术方法与途径,为机械动力传动系统监控诊断技术向更深层次发展和应用提供有效的技术支持。论文的主要研究内容包括:
1、针对机械动力传动系统故障诊断对特征提取有效性要求,重点研究基于核方法的非线性故障特征提取技术。提出基于核主成分分析的铁谱磨粒特征提取方法,较好地解决了铁谱磨粒参数非线性特征提取和磨粒特征维数压缩问题;提出基于核独立分量分析的直升机传动系统故障特征预处理技术,为提高直升机传动系统早期故障特征提取的有效性奠定了基础;提出基于核Fisher判别分析的特征提取技术,有效增强故障特征的分类特性和提高故障分类识别准确性;
2、针对大型机械动力传动系统存在的故障样本不足或称小样本故障诊断问题,深入研究了支持向量机故障诊断方法。从提高“一对一”、“一对多”两种多分类器算法的训练和分类速度考虑,研究了算法的改进方案;为解决故障诊断中因故障决策错误所造成损失和风险不同的问题,对标准SVM算法加以改进,提出了基于模糊隶属度函数的SVM等风险故障分类决策模型;在上述研究的基础上,结合机械动力传动系统在线故障诊断要求,提出了核基特征提取与SVM多故障层次分类检测模型,通过轴承故障检测与分类应用实例,对模型可行性、有效性进行了检验;
3、针对大型机械动力传动系统缺乏故障样本条件下的故障检测问题,提出基于支持向量数据描述的故障检测方法,深入研究了该方法用于故障检测时核函数的选择及其核函数参数对决策边界区域和检测精度的影响,提出了考虑误判损失不等条件下SVDD决策模型的改进方案。相关方法在某型直升机传动系统轴承故障检测中得到成功应用;
4、针对动力装置运行状态非线性特点和状态预测要求,提出了基于相空间重构和支持向量回归的动力装置状态趋势预测方法,其中,采用相空间重构技术对刻画机组状态及其变化的非线性时间序列进行分析,计算出最小嵌入维作为预测样本向量的维数,利用支持向量回归机对机组运行状态样本学习训练,得到机组状态预测模型,对模型的状态预测能力进行了深入研究。该方法在某型舰船动力装置主汽轮机的运行状态预测中得到成功应用,为大型复杂机械动力传动系统非线性状态预测和掌握运行状态变化趋势提供了有效的解决方案。
The large-scale mechanical power and transmission systems are the types of important technical equipment being used extensively in national defense and national economy realms; their safety and reliability are crucial. Because of a series of characteristics including complicated structures, serious states coupling among the different parts, a strong nonlinear on their condition behaviors, an abominable operational environments and being subject to the non-gauss noise and various indetermination factors, it is quite difficult to carry out the missions of the fault diagnosis accurately for these systems. As an important supporting technology of ensuring that the machines operate safely and reliably and their maintenance actives are organized by the scientific and reasonable manners, researches on the advanced techniques including condition monitoring, fault diagnosis and condition forecasting are of quite significance.
Based on analyzing the nonlinear characteristics and aiming at the technical requirements of nonlinear fault diagnosis and also combining the recently achievements deriving from related academic realms, the kernel-based diagnostic techniques including feature extraction, fault detection/recognition and decision-making, the operational condition forecasting for mechanical power and transmission systems are studied systemically and detailed. The main purpose of the research work is to investigate the new approaches to deal with the problems of fault identification and condition forecasting accurately for the systems. At the same time, the research work also aims at providing valid technical support for promoting the development and application of the fault diagnostic technology. The detailed contents and innovative works are shown in the following sections.
1. To meet the requirement of extracting the effective features for the fault diagnosis of the mechanical power and transmission systems, the kernel-based methods of nonlinear feature extraction are studied. Firstly, the method of the debris feature extraction based on KPCA is presented. It is mainly used to analyze the nonlinear related features and reduce the dimension of the debris parameters. Secondly, the preprocessing technique for the fault feature extraction based on KICA being used in the helicopter transmission system is put forward. The research result demonstrates that the KICA is an effective approach of extracting the features that reflect the incipient faults. Thirdly, the KFDA based method of fault feature extraction is presented. It is good at enhancing the performance of classification for the fault features and promoting the veracity of the fault recognition.
2. To solve the diagnostic problem based on a small quantity of fault sample existed in large-scale mechanical power and transmission systems, the SVM based method of the fault recognition and decision-making is studied in detail. To promote the performances of two schemes of multi-classifier combination including 'one against one' and 'one against rest', the improved methods are proposed respectively, which is good at the training efficiency and can keep the original precision in classification. Considering that the different mistakes of decision-making may lead to unequal loss and risk in fault diagnosis process for the mechanical power and transmission system, the equal risk SVM model based on fuzzy membership function for fault diagnosis is presented. To meet the requirements of online diagnosis for mechanical system, the SVM based multi-layer model for fault detection is studied. Its validity is confirmed by fault detection experiment on bearing fault detection and classification.
3. For the purpose of fault detection for the large-scale mechanical power and transmission systems under the condition of the lack of fault samples, a novel fault detection method based on the SVDD model is put forward. The influences on decision-making boundary and classification precision by choosing different kernel functions and the kernel parameters are deeply studied and analyzed. An improved SVDD based model for fault detection is presented and discussed, which considered unequal loss owing to two kinds of wrong decision-making. The presented method is used for fault detection of the bearings mounted in a helicopter's transmission system and its validity is proved by the experimental studies.
4. Aiming at nonlinear characteristics existed in running condition of the mechanical power devices and combining the support vector regression with phase space reconstruction theory, a novel condition forecasting method is put forward. It is mainly by means of analyzing the nonlinear time series depicted the running condition changes and calculating the minimize embedded dimension which is used for determining the dimension of the sample's vectors by using the phase space reconstruction technology. The model of support vector regression is employed to forecast the running condition and its change trend. The forecasting capability of the model is investigated in detail. The related techniques have been applied successfully in the running condition forecasting of the mechanical power device in a warship. The research results indicate that the presented methods can provide a valid solution for the large scale and complicated machines' condition forecasting.
引文
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