电子系统的故障预测与健康管理技术研究
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摘要
近年来因电子系统的关键模块或元件故障而引起的灾难性事故时常发生，导致大量的人力、物力与财力的损失，各国政府迫切需要能够对电子系统开展基于故障预测与健康管理的“视情维修”，以此避免传统“定时维修”的维修过剩或“事后维修”造成的巨大损失。由于缺乏对电子系统状态准确的判断和健康分析，从安全角度对电子系统进行了大量不必要的维修，导致运行成本大大提高，电子系统已故障后再进行维修往往已经造成不可挽回的损失。视情维修由于后勤保障规模小，经济可承受性好以及可避免重大事故等显著优势而具有良好的前景，它要求对电子系统的故障能够尽早监测和识别，以及具备对电子系统的健康进行管理、状态进行预测的能力。传统的故障诊断技术已经不能满足实际需要，正是基于这个原因，电子系统的故障预测和健康管理(Prognostic and HealthManagement／Monitoring，PHM)研究近些年引起国内外科研人员的极大兴趣。目前国内外学者在这方面已获的研究成果很少，尤其国内的研究才刚刚起步，因此电子系统的PHM技术将是今后研究的重点。
PHM核心技术主要包括状态监测与健康管理技术、模块或元件级故障诊断及状态预测等几个方面的内容，基于上述原因，论文主要研究工作有：
1．电子系统的状态监测与健康管理研究。状态监测与健康管理是决定电子系统是否维修的前提，占据非常重要的地位。由于电子系统此时尚未出现明显故障，要想尽早监测出早期故障并估计系统的健康状况，状态特征的提取是非常关键的。不同的电子系统表现的特征各异，采取的特征提取法就不一样。本论文分别以某模拟电路(代表简单电子系统)和某型雷达发射机(代表复杂电子系统)为例，对前者采取线性辨别分析(Linear Discriminant Analysis，LDA)提取其状态特征，对后者采取小波技术提取其状态特征，并与基于隐马尔可夫模型(Hidden MarkovModel，HMM)相结合，其中对离散隐马尔可夫模型设计了改进的训练算法。HMM作为状态监测器计算未知状态的KL距离，成功实现把微弱变化的早期故障过程转化为明显变化的KL距离，并用之来评价电子系统的健康状况，为视情维修提供依据。解决“是否维修”。
2．电子系统的模块级故障诊断研究。若电子系统需要维修，大型电子系统的其组成模块间存在错综复杂的关系，难以了解其故障传播机理。尤其是对故障树或多信号模型很难建立的复杂电子系统，贝叶斯网络更是具有无可代替的优势，可通过结构学习来实现对“黑盒子”系统的故障建模，参数学习来实现故障定位。本论文提出了一种新的结构学习算法，在离散粒子群算法中嵌入交叉和变异的操作，实验验证了该算法具有良好的学习精度和效率，为贝叶斯网络应用在复杂电子系统的故障诊断提供了可能，并以某型雷达发射机为例，给出了详细的设计步骤并实验验证了贝叶斯网络在复杂电子系统模块级故障诊断中的有效性。解决“故障在哪里”。
3．电子系统的元件级故障诊断研究。当更换模块或备件不足时，定位到电子系统的早期故障元件是必须的，对此本论文提出基于LDA与HMM的电路系统元件级故障诊断方法，并对LDA的不足提出改进措施。将该方法与BP网络及其它方法作了比较，实验验证该方法具有最佳故障识别能力，并对HMM的参数、类型及结构选择进行了详细的实验分析。针对单类故障特征包含信息有限的特点，本论文还设计了特征级融合的故障诊断法，通过提取多类特征，利用LDA巧妙实现多类特征的降维融合，并将HMM作为故障分类器。实验验证该方法提供了比任一单类特征与HMM结合后更高的故障识别率。解决“故障是什么”。
4．电子系统的状态预测研究。若电子系统不需要维修时，预测系统的状态就是必要的。状态预测是比故障诊断更高级的监测技术，是利用电子系统的历史信息实现对系统未来的状态和趋势作出估计以防灾难性故障的发生。本论文以某型雷达发射机和某电路系统为例，通过分析其关键测试信号的特点设计了改进的灰色预测模型，其中利用新陈代谢法使模型参数在线改变，采用粒子群算法选择最佳预测维数。实验结果证明，该预测模型具有良好的预测精度和预测性能。解决“何时会故障”。
In recent years, the failures in electronic system's essential module parts or the keycomponents often lead to the disastrous accidents occur, which cause the vast loss of themanpower, the material resource and the financial resource et al. All governmentsurgently need to realize Condition-Based Maintenance (CBM) on electronic systems,which is based on the fault prognostic and health management technique, and thistechnique can avoid the overmuch maintenance of traditional fixed-time maintenance orthe huge loss of subsequent maintenance. Because lacking accurate state judgment andhealth analysis on electronic systems, much nonessential maintenance have been donefrom the security angle which leads to much increase of the run cost. If we carry on thesubsequent maintenance, the losses can not be avoided in time. CBM owns someremarkable advantages such as the small logistics support scale, the good economywithstanding and the performance of avoiding significant incidents et al and has thegood prospect. It requests to be able to monitor and distinguish the faults of electronicsystem as early as possible, along with the ability of managing system's health andpredicting its state. Traditional fault diagnosis technology can not satisfy the actual need,just based on this reason study on fault Prognostic and Health Management (PHM)arouses the domestic and foreign researchers' enormous interest. At present the researchresults in this domain are very few especially the domestic research just is in start stage,therefore PHM will be the key research direction in the future.
PHM mainly includes some key points such as state monitoring and healthmanagement, module-level or component-level fault diagnosis and state prediction et al.Based on the above reasons, the main works of this dissertation are shown as follows:
1. Study on state monitoring and health management for electronic system. Statemonitor and health management is the premise of deciding whether electronic systemneeds to be maintained and is very important in the whole system. Electronic systemsdo not show obvious faulty features during this period. How to extract state features isvery essential if we want to monitor incipient faults as early as possible. Becauseelectronic systems own various characters, we should make different methods to extract their state features. The dissertation presents an analog circuit (which represents simpleelectronic system) and a radar transmitter (which represents complex electronic system)as examples respectively: using LDA to extract normal state features from the formerand using wavelet technology to the latter, then the processed features are used to formthe observation sequences sent to HMM, where an improved algorithm is presented totrain DHMM. HMM is used as the state monitor to calculate the KL distance ofunknown state, which shows that the proposed method can convert the unconspicuouschange of incipient fault process into the obvious change of KL distance successfully,based on which we can estimate the health status of electronic system accurately andprovide basis for CBM. This study answers question such as "whether should electronicsystem be repaired?".
2. Study on module-level fault diagnosis for electronic system. If electronic systemneeds to be repaired, there usually exists an anfractuous relation among its sub-modules,so it is very difficulty to understood electronic system's fault propagation mechanism.Especially for some complex electronic systems, their fault trees or multi-signal modescan not be built successfully, and then Bayesian network is the best choice. For "blackbox" system, the fault model can be built and the faults can be diagnosed successfullythrough learning both Bayesian network's structure and its parameters. The dissertationbrings forward a new structure learning algorithm which inserts both the cross operatingand the mutation operating into PSO algorithm. The experimental results show that theproposed algorithm has good precision and excellent efficiency, which provides thepossibility for Bayesian network to apply to diagnose faults in complex electronicsystems. Taking a radar transmitter as the example, the dissertation gives detailed designsteps and makes corresponding simulation and the experimental results show thatBayesian network is very effective to diagnose the faults in complex electronic system.This study answers question such as "where is the fault module?".
3. Study on component-level fault diagnosis for electronic system.It is necessary torecognize incipient faulty components while the replacement modules or spare parts areinsufficient, and the dissertation puts forward a novel method based on LDA and HMMto diagnose the incipient faults in analog circuit, where the performance of LDA isimproved through overcoming the shortcomings existing in the original LDA. Throughcomparing with BP network and some other methods, the experiment results show that the novel method has the best recognition capability. The dissertation also makesdetailed experimental analysis on selection of HMM's parameters, its types and itsstructures. Considering a kind of feature containing less fault information, thedissertation presents a fault diagnosis method based on feature fusion. Different kinds oforiginal feature vectors are extracted from analog circuit simultaneously, and then LDAis used to reduce the dimensions of the original feature vectors and remove theirredundancy together aiming at achieving their fusion skillfully. Finally HMM is used asthe classfier to accomplish the diagnosis of the incipient faults. The experimental resultsshow that the proposed method provides higher recognition rate compared to that of anykind of feature combined with HMM. This study answers question such as "what is thefaulty component?".
4. Study on state prediction for electronic system. It is necessary to predictelectronic system's state if it needs not to be repaired. State prediction is a highermonitoring technology compared to fault diagnosis. State prediction usually makes useof electronic system's historical information to estimate its future state and tendencyaiming at avoiding disastrous faults. The dissertation takes both a radar transmitter andan analog circuit as examples and an improved GM (1, 1) is used as the state predictorthrough analyzing their key testing signals' characters, where a metabolism method ispresented to make the model parameters on-line change and PSO algorithm is used toobtain the best forecast dimension. The improved model is tested and the experimentalresults show that the improved model has good precision and performance. This studyanswers question such as "when will the failure occur?".
PHM核心技术主要包括状态监测与健康管理技术、模块或元件级故障诊断及状态预测等几个方面的内容，基于上述原因，论文主要研究工作有：
1．电子系统的状态监测与健康管理研究。状态监测与健康管理是决定电子系统是否维修的前提，占据非常重要的地位。由于电子系统此时尚未出现明显故障，要想尽早监测出早期故障并估计系统的健康状况，状态特征的提取是非常关键的。不同的电子系统表现的特征各异，采取的特征提取法就不一样。本论文分别以某模拟电路(代表简单电子系统)和某型雷达发射机(代表复杂电子系统)为例，对前者采取线性辨别分析(Linear Discriminant Analysis，LDA)提取其状态特征，对后者采取小波技术提取其状态特征，并与基于隐马尔可夫模型(Hidden MarkovModel，HMM)相结合，其中对离散隐马尔可夫模型设计了改进的训练算法。HMM作为状态监测器计算未知状态的KL距离，成功实现把微弱变化的早期故障过程转化为明显变化的KL距离，并用之来评价电子系统的健康状况，为视情维修提供依据。解决“是否维修”。
2．电子系统的模块级故障诊断研究。若电子系统需要维修，大型电子系统的其组成模块间存在错综复杂的关系，难以了解其故障传播机理。尤其是对故障树或多信号模型很难建立的复杂电子系统，贝叶斯网络更是具有无可代替的优势，可通过结构学习来实现对“黑盒子”系统的故障建模，参数学习来实现故障定位。本论文提出了一种新的结构学习算法，在离散粒子群算法中嵌入交叉和变异的操作，实验验证了该算法具有良好的学习精度和效率，为贝叶斯网络应用在复杂电子系统的故障诊断提供了可能，并以某型雷达发射机为例，给出了详细的设计步骤并实验验证了贝叶斯网络在复杂电子系统模块级故障诊断中的有效性。解决“故障在哪里”。
3．电子系统的元件级故障诊断研究。当更换模块或备件不足时，定位到电子系统的早期故障元件是必须的，对此本论文提出基于LDA与HMM的电路系统元件级故障诊断方法，并对LDA的不足提出改进措施。将该方法与BP网络及其它方法作了比较，实验验证该方法具有最佳故障识别能力，并对HMM的参数、类型及结构选择进行了详细的实验分析。针对单类故障特征包含信息有限的特点，本论文还设计了特征级融合的故障诊断法，通过提取多类特征，利用LDA巧妙实现多类特征的降维融合，并将HMM作为故障分类器。实验验证该方法提供了比任一单类特征与HMM结合后更高的故障识别率。解决“故障是什么”。
4．电子系统的状态预测研究。若电子系统不需要维修时，预测系统的状态就是必要的。状态预测是比故障诊断更高级的监测技术，是利用电子系统的历史信息实现对系统未来的状态和趋势作出估计以防灾难性故障的发生。本论文以某型雷达发射机和某电路系统为例，通过分析其关键测试信号的特点设计了改进的灰色预测模型，其中利用新陈代谢法使模型参数在线改变，采用粒子群算法选择最佳预测维数。实验结果证明，该预测模型具有良好的预测精度和预测性能。解决“何时会故障”。
In recent years, the failures in electronic system's essential module parts or the keycomponents often lead to the disastrous accidents occur, which cause the vast loss of themanpower, the material resource and the financial resource et al. All governmentsurgently need to realize Condition-Based Maintenance (CBM) on electronic systems,which is based on the fault prognostic and health management technique, and thistechnique can avoid the overmuch maintenance of traditional fixed-time maintenance orthe huge loss of subsequent maintenance. Because lacking accurate state judgment andhealth analysis on electronic systems, much nonessential maintenance have been donefrom the security angle which leads to much increase of the run cost. If we carry on thesubsequent maintenance, the losses can not be avoided in time. CBM owns someremarkable advantages such as the small logistics support scale, the good economywithstanding and the performance of avoiding significant incidents et al and has thegood prospect. It requests to be able to monitor and distinguish the faults of electronicsystem as early as possible, along with the ability of managing system's health andpredicting its state. Traditional fault diagnosis technology can not satisfy the actual need,just based on this reason study on fault Prognostic and Health Management (PHM)arouses the domestic and foreign researchers' enormous interest. At present the researchresults in this domain are very few especially the domestic research just is in start stage,therefore PHM will be the key research direction in the future.
PHM mainly includes some key points such as state monitoring and healthmanagement, module-level or component-level fault diagnosis and state prediction et al.Based on the above reasons, the main works of this dissertation are shown as follows:
1. Study on state monitoring and health management for electronic system. Statemonitor and health management is the premise of deciding whether electronic systemneeds to be maintained and is very important in the whole system. Electronic systemsdo not show obvious faulty features during this period. How to extract state features isvery essential if we want to monitor incipient faults as early as possible. Becauseelectronic systems own various characters, we should make different methods to extract their state features. The dissertation presents an analog circuit (which represents simpleelectronic system) and a radar transmitter (which represents complex electronic system)as examples respectively: using LDA to extract normal state features from the formerand using wavelet technology to the latter, then the processed features are used to formthe observation sequences sent to HMM, where an improved algorithm is presented totrain DHMM. HMM is used as the state monitor to calculate the KL distance ofunknown state, which shows that the proposed method can convert the unconspicuouschange of incipient fault process into the obvious change of KL distance successfully,based on which we can estimate the health status of electronic system accurately andprovide basis for CBM. This study answers question such as "whether should electronicsystem be repaired?".
2. Study on module-level fault diagnosis for electronic system. If electronic systemneeds to be repaired, there usually exists an anfractuous relation among its sub-modules,so it is very difficulty to understood electronic system's fault propagation mechanism.Especially for some complex electronic systems, their fault trees or multi-signal modescan not be built successfully, and then Bayesian network is the best choice. For "blackbox" system, the fault model can be built and the faults can be diagnosed successfullythrough learning both Bayesian network's structure and its parameters. The dissertationbrings forward a new structure learning algorithm which inserts both the cross operatingand the mutation operating into PSO algorithm. The experimental results show that theproposed algorithm has good precision and excellent efficiency, which provides thepossibility for Bayesian network to apply to diagnose faults in complex electronicsystems. Taking a radar transmitter as the example, the dissertation gives detailed designsteps and makes corresponding simulation and the experimental results show thatBayesian network is very effective to diagnose the faults in complex electronic system.This study answers question such as "where is the fault module?".
3. Study on component-level fault diagnosis for electronic system.It is necessary torecognize incipient faulty components while the replacement modules or spare parts areinsufficient, and the dissertation puts forward a novel method based on LDA and HMMto diagnose the incipient faults in analog circuit, where the performance of LDA isimproved through overcoming the shortcomings existing in the original LDA. Throughcomparing with BP network and some other methods, the experiment results show that the novel method has the best recognition capability. The dissertation also makesdetailed experimental analysis on selection of HMM's parameters, its types and itsstructures. Considering a kind of feature containing less fault information, thedissertation presents a fault diagnosis method based on feature fusion. Different kinds oforiginal feature vectors are extracted from analog circuit simultaneously, and then LDAis used to reduce the dimensions of the original feature vectors and remove theirredundancy together aiming at achieving their fusion skillfully. Finally HMM is used asthe classfier to accomplish the diagnosis of the incipient faults. The experimental resultsshow that the proposed method provides higher recognition rate compared to that of anykind of feature combined with HMM. This study answers question such as "what is thefaulty component?".
4. Study on state prediction for electronic system. It is necessary to predictelectronic system's state if it needs not to be repaired. State prediction is a highermonitoring technology compared to fault diagnosis. State prediction usually makes useof electronic system's historical information to estimate its future state and tendencyaiming at avoiding disastrous faults. The dissertation takes both a radar transmitter andan analog circuit as examples and an improved GM (1, 1) is used as the state predictorthrough analyzing their key testing signals' characters, where a metabolism method ispresented to make the model parameters on-line change and PSO algorithm is used toobtain the best forecast dimension. The improved model is tested and the experimentalresults show that the improved model has good precision and performance. This studyanswers question such as "when will the failure occur?".
引文
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