面向结构健康监测的多主体协作融合方法研究
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摘要
近年来，结构健康监测技术作为智能材料结构研究的一个重要分支，在学术和工程化方面都得到了长足的发展。作为一项逐步从实验室阶段走向实用化的新技术，实际工程结构所需监测面积大、构型复杂，所布置的传感器种类多样，数目较多，每个传感器获得的信息是局部的、不完整的，加之环境恶劣，对传感器的可靠性也提出了挑战，并且目前各种结构损伤辨识方法也都存在着各自的不足和局限性。因此亟待解决的问题是如何管理协调庞大密集的传感器网络，如何融合不同种类的传感器信息，如何协作融合不同的损伤诊断方法，对整个结构的健康状态给出一个可靠有效的评估。本文利用多主体方法管理大型结构健康监测系统，深入研究了面向结构健康监测的多主体协作融合方法。
所进行的主要研究与创新点如下：
1）系统总结了主体及多主体原理，研究了多主体系统存在的冲突和消解协调策略以及协作策略，在此基础上，研究了多主体结构健康监测系统中的理论模型、体系结构和协作策略，针对大型结构传感器网络复杂损伤评估方法多样的特点，提出了基于区域监督主体和中央协调主体的联合协作融合多主体体系结构，从而将分布式和集中式监测相结合，为大型结构的多传感器多损伤监测研究奠定了基础。
2）针对大型结构多传感器多损伤如何自主监测问题，建立了多主体结构健康监测系统，针对大型结构的三种典型损伤形式：结构应力应变分布变化、紧固件连接失效、冲击载荷，以大型航空铝板为研究对象，采用压电传感器、光纤传感器、应变传感器网络和相应采集系统，协作自组织传感器网络，自动选择诊断方法，从而对整个大型结构的损伤状况给出一个有效的评估。
3）针对大型结构传感网络如何快速有效覆盖大型结构的各个子区域损伤，以及如何协调融合利用各种算法对各种损伤给出可靠有效的评估，提出了基于多主体黑板协作的多区域监测框架。在此基础上，针对大型结构的分布式应变分布以及损伤分类监测问题，提出了应变传感器网络的黑板协作组网方法和损伤分类评估的合同网协作融合方法；针对大型结构的分布式声源和冲击定位问题，提出了压电传感器网络的黑板协作组网方法和利用黄页服务进行声发射定位的数据融合策略，在大型航空铝板结构上验证该方法能够快速有效利用分布式信号信息处理算法资源，提高损伤定位的精度。
4）针对大型结构冲击载荷定位准确快速辨识问题，在改进了基于切比雪夫多项式的时域载荷反演优化问题的基础上，提出了基于三角测量方法和时域载荷反演方法的多主体黑板协作冲击定位方法，在大型航空铝板结构和复合材料板上验证该方法在保证冲击定位精度的同时，提高了实时性。
5）针对大型结构紧固件失效准确快速辨识问题，提出了基于互信息分类器选择和多主体决策融合的紧固件连接失效辨识方法。首先，研究了分类器选择指标和方法，提出了基于熵的互信息相关度为标准的分类选择算法流程；然后，研究了多分类器融合的常用方法，提出了基于置信、通信、协商主体模型，在此基础上进而提出了多主体决策融合方法，在大型航空铝板和航空铝制加筋板验证了该方法可以利用最少的分类器资源和计算时间，获得最优的损伤识别精度。
6）针对传感器失效情况下的结构健康监测系统不能精确损伤评估问题，提出了多主体推理协作的压电激励传感网络自诊断自重构的损伤监测方法。首先，分析了传感器自诊断自重构的研究现状，提出了基于主动监测方法和压电元件脱粘失效因子来评估压电元件脱粘失效，并结合主体推理进行压电网络中失效元件的自诊断方法；然后，提出了主被动损伤监测情况下的压电网络的自重构方法，以及复用压电元件的主被动冲突消解方法，在航空铝板结构上验证了该方法能准确识别失效传感器，保证损伤定位精度。
本文工作在机械结构力学及控制国家重点实验室完成。
Recently, structural health monitoring (SHM) technology is a research focus in the engineering and academic domain. For the actual large-scale structure monitoring, there are a number of sensors and various ones, which are distributed and dispersed, and the various evaluation methods. For the application, the integration of a wide range of sensors and different evaluation methods must be done. Hence, using the SHM technology on the large complicated practical structures, it is a critical problem that how to effectively manage the distributed sensor network, and coordinate and fusion distributed information and different evaluation methods for the efficiency evaluation of the system. In this dissertation, the multi-agent system (MAS) in Artificial Intelligence (AI) area is adopted to manage the large SHM, in which multi-agent coordination and fusion is intensively studied.
The main works and novel researches performed in this dissertation include:
1) The theory of the agent and MAS is summarized, and the conflict resolution and coordination strategy of the MAS is studied. Based on the theory, the agent model, the architecture and coordination of MAS based SHM are researched. Since there exits the complex sensor network and various damage assessment methods in a large SHM system, the architecture of MAS based on district monitor agent and central coordination agent is proposed, combining with the distributed and centralized structure. The architecture is the research foundation for large SHM with multi-sensor and multi-damage.
2) To autonomously monitor the large structure with multi-sensor and multi-damage, a MAS based SHM is built. For three typical kinds of structure damages including strain distribution change, joint failure and impact load in the large-scale aviation aluminum plate structure, the system is developed with piezoelectric sensor, FBG sensor and strain gauge sensor, and their acquirement system. Multi-agent coordination is adopted to self-organize the sensor network and automatically choose sensing object, choose suitable signal processing method and damage evaluation method to recognize three typical structural states. At last, an effective evaluation is given for the damge state of the large structure.
3) To fast and effectively cover each sub-region damage with sensor network in large structure, as well as to coordinate and fuse various damage identification algorithms to give a reliable and effective assessment for all kinds of damage, the multi-region monitoring architecture based on the multi-agent blackboard coordination is proposed for the large-scale structure. Based on the architecture, for the large-scale distributed structural strain distribution and damage classification monitoring, the self-organization network method based on the strain sensor network and the blackboard coordination, and the contract net coordination and fusion method based on the damage classification evaluation is proposed. Also, for large-scale structural distributed acoustic emission source and impact location, the self-organization network method based on piezoelectric sensor network and the blackboard coordination, and the server directory coordination and data fusion method based on the emission source location is proposed. The experimental verification is conducted on a large aviation aluminum plate to validate the efficiencies of the resource allocation and damage location.
4) To improve the accuracy and real-time of the large structural impact load location, based on the improved inverse analysis method in time domain with Chebyshev polynomial basis function, for the advantages and disadvantages of the acoustic emission and the inverse analysis methods, a precise impact positioning method based multi-agent blackboard coordination is proposed. A large aviation aluminum plate and T-30carbon fiber laminated composite plate experiments show that the impact positioning method is fast and efficient, with good robustness.
5) To improve the accuracy and real-time of the large structural joint failure identification, the identification method based on the classifier choice of the mutual information and the multi-agent decision fusion is studied. Firstly, the indicator and the algorithm of classifier choice are studied, and the algorithm flow of the classifier choice based on the mutual information degree of correlation is proposed. Secondly, the commonly used methods of the classifier combination are studied, and the multi-agent decision fusion based on the agent reasoning model with confidence, communication and coordination is presented. At last, a large aviation aluminum plate and aviation aluminum stiffened plate experiments show that the method can accurately and rapidly identify the damage.
6) To accurately assess the structural damage for the SHM system with the sensor failure, the damage monitoring method of piezoelectric sensor network self-diagnosis and self-configuration based on the multi-agent reasoning and collaboration is put forward. Firstly, the research on the sensor self-diagnosis and self-configuration is analysed. Then, the multi-agent active and passive coordination monitoring based on piezoelectric sensor network self-diagnosis and self-configuration is presented to identify the debonding sensor with the active monitoring and the debonding fault factor, and coordinate to self-configurate the normal piezoelectric sensor network to obtain the active and passive evaluation of damage on the basis of the conflict resolution on the shared piezoelectric sensor. At last, an aviation aluminum plate experiment shows that the method is efficient.
This work is completed in State Key Laboratory of Mechanics and Control of Mechanical (?)
所进行的主要研究与创新点如下：
1）系统总结了主体及多主体原理，研究了多主体系统存在的冲突和消解协调策略以及协作策略，在此基础上，研究了多主体结构健康监测系统中的理论模型、体系结构和协作策略，针对大型结构传感器网络复杂损伤评估方法多样的特点，提出了基于区域监督主体和中央协调主体的联合协作融合多主体体系结构，从而将分布式和集中式监测相结合，为大型结构的多传感器多损伤监测研究奠定了基础。
2）针对大型结构多传感器多损伤如何自主监测问题，建立了多主体结构健康监测系统，针对大型结构的三种典型损伤形式：结构应力应变分布变化、紧固件连接失效、冲击载荷，以大型航空铝板为研究对象，采用压电传感器、光纤传感器、应变传感器网络和相应采集系统，协作自组织传感器网络，自动选择诊断方法，从而对整个大型结构的损伤状况给出一个有效的评估。
3）针对大型结构传感网络如何快速有效覆盖大型结构的各个子区域损伤，以及如何协调融合利用各种算法对各种损伤给出可靠有效的评估，提出了基于多主体黑板协作的多区域监测框架。在此基础上，针对大型结构的分布式应变分布以及损伤分类监测问题，提出了应变传感器网络的黑板协作组网方法和损伤分类评估的合同网协作融合方法；针对大型结构的分布式声源和冲击定位问题，提出了压电传感器网络的黑板协作组网方法和利用黄页服务进行声发射定位的数据融合策略，在大型航空铝板结构上验证该方法能够快速有效利用分布式信号信息处理算法资源，提高损伤定位的精度。
4）针对大型结构冲击载荷定位准确快速辨识问题，在改进了基于切比雪夫多项式的时域载荷反演优化问题的基础上，提出了基于三角测量方法和时域载荷反演方法的多主体黑板协作冲击定位方法，在大型航空铝板结构和复合材料板上验证该方法在保证冲击定位精度的同时，提高了实时性。
5）针对大型结构紧固件失效准确快速辨识问题，提出了基于互信息分类器选择和多主体决策融合的紧固件连接失效辨识方法。首先，研究了分类器选择指标和方法，提出了基于熵的互信息相关度为标准的分类选择算法流程；然后，研究了多分类器融合的常用方法，提出了基于置信、通信、协商主体模型，在此基础上进而提出了多主体决策融合方法，在大型航空铝板和航空铝制加筋板验证了该方法可以利用最少的分类器资源和计算时间，获得最优的损伤识别精度。
6）针对传感器失效情况下的结构健康监测系统不能精确损伤评估问题，提出了多主体推理协作的压电激励传感网络自诊断自重构的损伤监测方法。首先，分析了传感器自诊断自重构的研究现状，提出了基于主动监测方法和压电元件脱粘失效因子来评估压电元件脱粘失效，并结合主体推理进行压电网络中失效元件的自诊断方法；然后，提出了主被动损伤监测情况下的压电网络的自重构方法，以及复用压电元件的主被动冲突消解方法，在航空铝板结构上验证了该方法能准确识别失效传感器，保证损伤定位精度。
本文工作在机械结构力学及控制国家重点实验室完成。
Recently, structural health monitoring (SHM) technology is a research focus in the engineering and academic domain. For the actual large-scale structure monitoring, there are a number of sensors and various ones, which are distributed and dispersed, and the various evaluation methods. For the application, the integration of a wide range of sensors and different evaluation methods must be done. Hence, using the SHM technology on the large complicated practical structures, it is a critical problem that how to effectively manage the distributed sensor network, and coordinate and fusion distributed information and different evaluation methods for the efficiency evaluation of the system. In this dissertation, the multi-agent system (MAS) in Artificial Intelligence (AI) area is adopted to manage the large SHM, in which multi-agent coordination and fusion is intensively studied.
The main works and novel researches performed in this dissertation include:
1) The theory of the agent and MAS is summarized, and the conflict resolution and coordination strategy of the MAS is studied. Based on the theory, the agent model, the architecture and coordination of MAS based SHM are researched. Since there exits the complex sensor network and various damage assessment methods in a large SHM system, the architecture of MAS based on district monitor agent and central coordination agent is proposed, combining with the distributed and centralized structure. The architecture is the research foundation for large SHM with multi-sensor and multi-damage.
2) To autonomously monitor the large structure with multi-sensor and multi-damage, a MAS based SHM is built. For three typical kinds of structure damages including strain distribution change, joint failure and impact load in the large-scale aviation aluminum plate structure, the system is developed with piezoelectric sensor, FBG sensor and strain gauge sensor, and their acquirement system. Multi-agent coordination is adopted to self-organize the sensor network and automatically choose sensing object, choose suitable signal processing method and damage evaluation method to recognize three typical structural states. At last, an effective evaluation is given for the damge state of the large structure.
3) To fast and effectively cover each sub-region damage with sensor network in large structure, as well as to coordinate and fuse various damage identification algorithms to give a reliable and effective assessment for all kinds of damage, the multi-region monitoring architecture based on the multi-agent blackboard coordination is proposed for the large-scale structure. Based on the architecture, for the large-scale distributed structural strain distribution and damage classification monitoring, the self-organization network method based on the strain sensor network and the blackboard coordination, and the contract net coordination and fusion method based on the damage classification evaluation is proposed. Also, for large-scale structural distributed acoustic emission source and impact location, the self-organization network method based on piezoelectric sensor network and the blackboard coordination, and the server directory coordination and data fusion method based on the emission source location is proposed. The experimental verification is conducted on a large aviation aluminum plate to validate the efficiencies of the resource allocation and damage location.
4) To improve the accuracy and real-time of the large structural impact load location, based on the improved inverse analysis method in time domain with Chebyshev polynomial basis function, for the advantages and disadvantages of the acoustic emission and the inverse analysis methods, a precise impact positioning method based multi-agent blackboard coordination is proposed. A large aviation aluminum plate and T-30carbon fiber laminated composite plate experiments show that the impact positioning method is fast and efficient, with good robustness.
5) To improve the accuracy and real-time of the large structural joint failure identification, the identification method based on the classifier choice of the mutual information and the multi-agent decision fusion is studied. Firstly, the indicator and the algorithm of classifier choice are studied, and the algorithm flow of the classifier choice based on the mutual information degree of correlation is proposed. Secondly, the commonly used methods of the classifier combination are studied, and the multi-agent decision fusion based on the agent reasoning model with confidence, communication and coordination is presented. At last, a large aviation aluminum plate and aviation aluminum stiffened plate experiments show that the method can accurately and rapidly identify the damage.
6) To accurately assess the structural damage for the SHM system with the sensor failure, the damage monitoring method of piezoelectric sensor network self-diagnosis and self-configuration based on the multi-agent reasoning and collaboration is put forward. Firstly, the research on the sensor self-diagnosis and self-configuration is analysed. Then, the multi-agent active and passive coordination monitoring based on piezoelectric sensor network self-diagnosis and self-configuration is presented to identify the debonding sensor with the active monitoring and the debonding fault factor, and coordinate to self-configurate the normal piezoelectric sensor network to obtain the active and passive evaluation of damage on the basis of the conflict resolution on the shared piezoelectric sensor. At last, an aviation aluminum plate experiment shows that the method is efficient.
This work is completed in State Key Laboratory of Mechanics and Control of Mechanical (?)
引文
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