光纤结构健康监测系统及其传感器网络可靠性研究
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摘要
基于光纤布拉格光栅（Fiber Bragg Grating, FBG）传感器网络的结构健康监测系统因其突出的优点在土木工程和航空航天领域受到了大家的青睐。但在实际工程应用中，FBG传感器网络通常埋置于结构中，一旦发生故障很难进行更换，从而影响结构健康监测系统的性能。为了解决上述问题，本文研究内容围绕结构健康监测系统及提高系统中光纤传感器网络可靠性的关键技术依次展开，探索提高光纤结构健康监测系统中的损伤识别精度、容错能力及其传感器网络的可靠性方法。
首先，采用耦合模理论、传输矩阵法、色心模型、有限包层半径布拉格光栅理论，研究了FBG传感器在使用中因疲劳应变、高/低温、紫外线漂白和化学腐蚀等作用性能发生退化的原理，并借助光波导数值分析软件OptiFDTD对FBG传感器性能发生退化时的信号特征进行了分析，为及时准确判别与评估FBG传感器的健康状况提供依据。接着以FBG传感器网络监测飞机机翼盒段试验件上的静态载荷和碳纤维复合材料板试验件上的动态载荷为研究对象，对基于支持向量机算法的结构损伤定位方法进行了研究，重点研究了支持向量机参数优化方法，分别采用网格搜索算法、遗传算法和粒子群优化算法对支持向量机的参数进行优化，并对三种方法的性能进行了分析对比。在以上损伤定位研究的基础上，进一步研究了FBG传感器网络故障情况下如何避免系统性能严重退化，提高结构健康监测系统的可靠性。在FBG传感器网络故障的情况下，研究了通过动态修改支持向量机模型和支持向量机参数来提高损伤定位精度。结果表明：以8个FBG传感器组成的网络为例的静态载荷和动态载荷结构健康监测系统，当网络中1个或2个FBG传感器信号失效时，其识别精度同传感器网络完好时的识别精度保持一致。因此，采用模型重构算法可以显著提高传感器故障时系统的监测精度，提高了结构健康监测系统的可靠性。
其次，研究了通过优化结构健康监测系统中FBG传感器网络的排布位置提高结构健康监测系统的可靠性。以机翼形状铝合金板试验件为研究对象，以不同FBG传感器数据之间的相关系数为依据，研究采用逐步累积法和支持向量回归机优化FBG传感器的布置位置和数量。首先在监测对象上设置足够多的传感器预布置点，并对被测对象进行加载，获得每个监测点(即传感器预布置点)与所有加载点之间的相关性，然后从所有监测点挑选一个点作为第一个传感器布置点，接着从剩余预布置点中挑选和第一个相关性最小的点作为第二个传感器布置点，依次类推，从剩余预布置点中挑选和已选点相关性均比较小的点作为下一个传感器布置点。每增加一个点需要计算一次采用已选传感器组合进行监测时的系统的性能，以此作为评价所选传感器组合的标准，直至已选传感器的位置和数量满足要求。结果表明：对应于初始布置模式下传感器网络的布置方案，优化后的传感器布置方案可以在保证系统监测精度的情况下采用更少的传感器，采用相同数量的传感器可以获得更高的监测精度。
接着，研究了通过优化结构健康监测系统中FBG传感器网络的拓扑结构提高结构健康监测系统的可靠性。在传统FBG传感器网络中引入光导开关，并采用图论相关理论建立传感器网络的拓扑模型，当网络中某个传感器或传输光纤发生故障时，通过分析网络模型的邻接矩阵确定光开关切换顺序，从而为网络中依然存活的传感节点重新寻找新的传输路径，使故障对网络中其余传感器的影响减到最小。同时，根据可靠性相关理论，在模型重构算法的基础上，分析对比了传统的光纤传感器网络拓扑结构和引入光导开关后的网络拓扑结构的可靠性，分析结果表明：引入光开关后的网络拓扑结构的可靠性明显高于传统网络拓扑结构的可靠性，单个传感器的失效概率不同，两种网络可靠性差别也不同；当单个元器件的失效概率在0.001和0.01之间变动时，若系统允许距离误差在40mm以内的预测点占总测试数据的比例达到95%，则引入光开关后的FBG传感器网络拓扑结构的失效率降低为传统网络拓扑结构失效率的50%；若系统允许距离误差在40mm以内的预测点占总测试数据的比例达到90%，则引入光开关后的FBG传感器网络的失效率至少可以降低为传统网络拓扑结构失效率的12.5%。
最后，研究了采用多主体技术提高FBG传感器网络的可靠性。针对FBG传感器网络的结构健康监测系统设计了多主体框架结构、主体的结构形式和功能、主体之间的通信方式和数据融合方式，并结合FBG传感器网络监测飞机机翼盒段试验件上的静态载荷为研究对象，对双主体协作进行了重点研究。首先根据研究的传感器优化排布方法，确定了每个主体内部的传感器数量和排布位置。接着分别研究两个主体均正常和一个主体故障情况下多主体融合结果。当两个主体均完好时，直接采用加权平均法对两个主体的识别结果进行融合；当某个主体发生故障时，故障主体内部首先采用模型重构的方法对失效信号进行补偿，在此基础上再对两个主体的结果进行加权平均，获得最终的识别结果。研究结果表明：采用多主体技术不仅可以显著提高外部载荷位置的预测精度，而且提高了整个系统在故障情况下的预测性能，提高了整个系统的可靠性。
The intelligent structural health monitoring system based on the fiber Bragg grating (FBG)sensor network has been widely used in the civil engineering and aerospace fields because of itsoutstanding advantages. In practical engineering, FBG sensors are usually embedded in thestructure, once the sensing or transmission optical fibers are breakage or invalid, it is difficult torenew the invalid point. Therefore, the performance of the structural health monitoring will beaffected. For solving above problems, the optical fiber structural health monitoring system and thereliability of its sensor network are researched in this paper, the method that how to improve thedamage detection precision, fault-tolerant ability and FBG sensor netwok reliability are explored.
Firstly, depending on the coupled-mode theory, Transfer matrix, color center model, limitedcladding radius Bragg grating theory, the principle of the performance degradation when the FBGsensor is suffered strain fatigue, high/low temperature, ultraviolet bleaching, chemical corrosionetc. are researched. By virtue of the optical waveguide numerical analysis software OptiFDTD, thesignal characteristic of the main parameters degenerated FBG sensor is analyzed, the resultsprovide a certain reference for judging and assessing the healthy situation of the FBG sensornetwork. Next, with the static load on the typical structure of the plane wing box test pannel andthe dynamic load on the Carbon Fiber composite structure test pannel as subjects seperately, byvirtue of the FBG sensor network signals embeded in the structure, the loading damagelocalization algorithm based on the support vector machine is researched, and the parametersoptimization method of the support vector is studied detailly. The grid search algorithm, geneticalgorithm and particle swarm optimization algorithm are proposed to optimize the mainparameters of the support vector machine, and the performances of the three mathods arecompared in this paper. Depending on the above research method, the reliability method thatpartial FBG sensors are invalid in the structural health monitoring system is researched. Forenhancing the damaging localization precision when the FBG sensors or fiber nodes are invalid inthe network, the model reconstruction which the support vector machine models and parametersare modified dynamically is research. The research results indicate that the proposed modelreconstruction algorithm based on above three damaging localization algorithm can almost keepthe predicting precision when no sensor, one sensor and two sensors are invalid in the structuralhealth monitoring, thus the reliability is improved when there are FBG sensors are invalid in thestructural health monitoring system.
Secondly, for improving the reliability of the structural health monitoring, the optimumarrangement of the FBG sensor in the aviation structure is researched. The wing shape aluminum alloy test pannel is taken as research object, by virtue of the correlation coefficient betweendifferent FBG sensor data, the gradual accumulation method and support vector machine are usedto optimize the arrangement placement and numbers of the FBG sensors. First of all, enoughpre-arrangement positions are set in the monitored structure, and certain loads are applied on thedifferent positions of the structure. Thus the correlation coefficient between the loading andmonitoring (that is to say sensor pre-arrangement) positions are obtained. Then, one of position ischoiced as the first sensor arrangement position from all of the monitoring position. Next, thesmallest correlation coefficient relative to the first sensor position is choiced as the second sensorarrangement position. And so on, the smaller correlation coefficient relative to the selectedposition is choiced as the next sensor arrangement position. When one sensor arrangementposition is added to the selected sensor combination, the performance of the monitoring system iscomputed by the support vector machine, and the predicting accuracy is considered as theevaluation standard of selected sensor combination, until the positions and the numbers of theselected sensor meet the monitoring requirement. The research results indicate that the optimalarrangement scheme of the FBG sensor network relative to the initial arrangement mode isobtained, and the monitoring system predicting accuracy can be obtained with fewer FBG sensorsin the optimal arrangement scheme. Simultanely, with the same numbers of FBG sensors, theoptimal arrangement scheme can obtain higher monitoring accuracy.
Thirdly, the high reliability structural health monitoring system based on the optimum FBGsensor network topology is researched. The optical switch is introduced in the traditional network,and the sensor network topological model is built depending on the adjacency matrix in the graphtheory, if there are some sensors or transmitted optical fiber invalid, the adjacency matrix of theFBG sensor network topological model is analyzed to ensure the switch sequence of the opticalswitch, thus the transmission path for the survival sensor node can be obtained again, and theinfluence of the invalid point to the sensor network signals are minimize to the smallest.Meanwhile, depending on the related reliability theory, on the basis of model reconstruction, thereliability of traditional and introducing optical switch FBG sensor network are compared andanalyzed separately. The research results indicate that the reliability of the introducing opticalswitch FBG sensor topology is higher than the traditional one obviously. When the failure rate ofthe single component is diffeent, the reliability of the two network topology is also different.When the single component failure rate change between0.001and0.01, if the ratio that thedistance error between the actual value and predicting value less than or equal to40mm countsdivide total sample counts reachs to95%, the failure rate of the introducing optical switch sensornetwork topology is reduced to50%than the traditional one. If the ratio that the distance error between the actual value and predicting value less than or equal to40mm counts divide totalsample counts reachs to90%, the failure rate of the introducing optical switch sensor network isreduced to12.5%at least than the traditional one.
Finally, the high reliability FBG sensor network based on multi-agent technique is researched.Not only the multi-agent framework of the structure health monitoring system based on the FBGsensor network is designed, but also the agent’s structure and function, communication mode anddata fusion method between agents are designed, together with the static load on the plane wingbox test pannel as subject，the double agent cooperation is studied. In the first place, depending onthe sensor network optimization method above proposed, the arrangement position and number ofthe FBG sensor network adhered to the structure is determined. Following, the agent fusion resultthat each FBG sensor is in good condition in the agent and partial sensors can’t work as normal orsome sensors signal can’t be acquired in one agent is demonstrated separately. If all of the FBGsensor signals can be acquired correctly in each agent, the weighted average data fusion method isused to fuse the predicting result of the two agents directly for each external loading damageposition. If partial sensor signals can’t be acquired in one agent, at first, the model reconstructionalgorithm above proposed is used to compensate the invalid sensor signals in the faulted agent, onthat basis, the weighted average data fusion method is used to fuse the predicting result of the twoagents for each external loading damage, and the final predicting results are obtained for thestructural health monitoring. The research results indicate that the predicting accuracy of theexternal loading position is not only improved by the multi-agent technique, but also the reliabilityof the structural health monitoring system if partial sensors are invalid in the network is improved.
首先，采用耦合模理论、传输矩阵法、色心模型、有限包层半径布拉格光栅理论，研究了FBG传感器在使用中因疲劳应变、高/低温、紫外线漂白和化学腐蚀等作用性能发生退化的原理，并借助光波导数值分析软件OptiFDTD对FBG传感器性能发生退化时的信号特征进行了分析，为及时准确判别与评估FBG传感器的健康状况提供依据。接着以FBG传感器网络监测飞机机翼盒段试验件上的静态载荷和碳纤维复合材料板试验件上的动态载荷为研究对象，对基于支持向量机算法的结构损伤定位方法进行了研究，重点研究了支持向量机参数优化方法，分别采用网格搜索算法、遗传算法和粒子群优化算法对支持向量机的参数进行优化，并对三种方法的性能进行了分析对比。在以上损伤定位研究的基础上，进一步研究了FBG传感器网络故障情况下如何避免系统性能严重退化，提高结构健康监测系统的可靠性。在FBG传感器网络故障的情况下，研究了通过动态修改支持向量机模型和支持向量机参数来提高损伤定位精度。结果表明：以8个FBG传感器组成的网络为例的静态载荷和动态载荷结构健康监测系统，当网络中1个或2个FBG传感器信号失效时，其识别精度同传感器网络完好时的识别精度保持一致。因此，采用模型重构算法可以显著提高传感器故障时系统的监测精度，提高了结构健康监测系统的可靠性。
其次，研究了通过优化结构健康监测系统中FBG传感器网络的排布位置提高结构健康监测系统的可靠性。以机翼形状铝合金板试验件为研究对象，以不同FBG传感器数据之间的相关系数为依据，研究采用逐步累积法和支持向量回归机优化FBG传感器的布置位置和数量。首先在监测对象上设置足够多的传感器预布置点，并对被测对象进行加载，获得每个监测点(即传感器预布置点)与所有加载点之间的相关性，然后从所有监测点挑选一个点作为第一个传感器布置点，接着从剩余预布置点中挑选和第一个相关性最小的点作为第二个传感器布置点，依次类推，从剩余预布置点中挑选和已选点相关性均比较小的点作为下一个传感器布置点。每增加一个点需要计算一次采用已选传感器组合进行监测时的系统的性能，以此作为评价所选传感器组合的标准，直至已选传感器的位置和数量满足要求。结果表明：对应于初始布置模式下传感器网络的布置方案，优化后的传感器布置方案可以在保证系统监测精度的情况下采用更少的传感器，采用相同数量的传感器可以获得更高的监测精度。
接着，研究了通过优化结构健康监测系统中FBG传感器网络的拓扑结构提高结构健康监测系统的可靠性。在传统FBG传感器网络中引入光导开关，并采用图论相关理论建立传感器网络的拓扑模型，当网络中某个传感器或传输光纤发生故障时，通过分析网络模型的邻接矩阵确定光开关切换顺序，从而为网络中依然存活的传感节点重新寻找新的传输路径，使故障对网络中其余传感器的影响减到最小。同时，根据可靠性相关理论，在模型重构算法的基础上，分析对比了传统的光纤传感器网络拓扑结构和引入光导开关后的网络拓扑结构的可靠性，分析结果表明：引入光开关后的网络拓扑结构的可靠性明显高于传统网络拓扑结构的可靠性，单个传感器的失效概率不同，两种网络可靠性差别也不同；当单个元器件的失效概率在0.001和0.01之间变动时，若系统允许距离误差在40mm以内的预测点占总测试数据的比例达到95%，则引入光开关后的FBG传感器网络拓扑结构的失效率降低为传统网络拓扑结构失效率的50%；若系统允许距离误差在40mm以内的预测点占总测试数据的比例达到90%，则引入光开关后的FBG传感器网络的失效率至少可以降低为传统网络拓扑结构失效率的12.5%。
最后，研究了采用多主体技术提高FBG传感器网络的可靠性。针对FBG传感器网络的结构健康监测系统设计了多主体框架结构、主体的结构形式和功能、主体之间的通信方式和数据融合方式，并结合FBG传感器网络监测飞机机翼盒段试验件上的静态载荷为研究对象，对双主体协作进行了重点研究。首先根据研究的传感器优化排布方法，确定了每个主体内部的传感器数量和排布位置。接着分别研究两个主体均正常和一个主体故障情况下多主体融合结果。当两个主体均完好时，直接采用加权平均法对两个主体的识别结果进行融合；当某个主体发生故障时，故障主体内部首先采用模型重构的方法对失效信号进行补偿，在此基础上再对两个主体的结果进行加权平均，获得最终的识别结果。研究结果表明：采用多主体技术不仅可以显著提高外部载荷位置的预测精度，而且提高了整个系统在故障情况下的预测性能，提高了整个系统的可靠性。
The intelligent structural health monitoring system based on the fiber Bragg grating (FBG)sensor network has been widely used in the civil engineering and aerospace fields because of itsoutstanding advantages. In practical engineering, FBG sensors are usually embedded in thestructure, once the sensing or transmission optical fibers are breakage or invalid, it is difficult torenew the invalid point. Therefore, the performance of the structural health monitoring will beaffected. For solving above problems, the optical fiber structural health monitoring system and thereliability of its sensor network are researched in this paper, the method that how to improve thedamage detection precision, fault-tolerant ability and FBG sensor netwok reliability are explored.
Firstly, depending on the coupled-mode theory, Transfer matrix, color center model, limitedcladding radius Bragg grating theory, the principle of the performance degradation when the FBGsensor is suffered strain fatigue, high/low temperature, ultraviolet bleaching, chemical corrosionetc. are researched. By virtue of the optical waveguide numerical analysis software OptiFDTD, thesignal characteristic of the main parameters degenerated FBG sensor is analyzed, the resultsprovide a certain reference for judging and assessing the healthy situation of the FBG sensornetwork. Next, with the static load on the typical structure of the plane wing box test pannel andthe dynamic load on the Carbon Fiber composite structure test pannel as subjects seperately, byvirtue of the FBG sensor network signals embeded in the structure, the loading damagelocalization algorithm based on the support vector machine is researched, and the parametersoptimization method of the support vector is studied detailly. The grid search algorithm, geneticalgorithm and particle swarm optimization algorithm are proposed to optimize the mainparameters of the support vector machine, and the performances of the three mathods arecompared in this paper. Depending on the above research method, the reliability method thatpartial FBG sensors are invalid in the structural health monitoring system is researched. Forenhancing the damaging localization precision when the FBG sensors or fiber nodes are invalid inthe network, the model reconstruction which the support vector machine models and parametersare modified dynamically is research. The research results indicate that the proposed modelreconstruction algorithm based on above three damaging localization algorithm can almost keepthe predicting precision when no sensor, one sensor and two sensors are invalid in the structuralhealth monitoring, thus the reliability is improved when there are FBG sensors are invalid in thestructural health monitoring system.
Secondly, for improving the reliability of the structural health monitoring, the optimumarrangement of the FBG sensor in the aviation structure is researched. The wing shape aluminum alloy test pannel is taken as research object, by virtue of the correlation coefficient betweendifferent FBG sensor data, the gradual accumulation method and support vector machine are usedto optimize the arrangement placement and numbers of the FBG sensors. First of all, enoughpre-arrangement positions are set in the monitored structure, and certain loads are applied on thedifferent positions of the structure. Thus the correlation coefficient between the loading andmonitoring (that is to say sensor pre-arrangement) positions are obtained. Then, one of position ischoiced as the first sensor arrangement position from all of the monitoring position. Next, thesmallest correlation coefficient relative to the first sensor position is choiced as the second sensorarrangement position. And so on, the smaller correlation coefficient relative to the selectedposition is choiced as the next sensor arrangement position. When one sensor arrangementposition is added to the selected sensor combination, the performance of the monitoring system iscomputed by the support vector machine, and the predicting accuracy is considered as theevaluation standard of selected sensor combination, until the positions and the numbers of theselected sensor meet the monitoring requirement. The research results indicate that the optimalarrangement scheme of the FBG sensor network relative to the initial arrangement mode isobtained, and the monitoring system predicting accuracy can be obtained with fewer FBG sensorsin the optimal arrangement scheme. Simultanely, with the same numbers of FBG sensors, theoptimal arrangement scheme can obtain higher monitoring accuracy.
Thirdly, the high reliability structural health monitoring system based on the optimum FBGsensor network topology is researched. The optical switch is introduced in the traditional network,and the sensor network topological model is built depending on the adjacency matrix in the graphtheory, if there are some sensors or transmitted optical fiber invalid, the adjacency matrix of theFBG sensor network topological model is analyzed to ensure the switch sequence of the opticalswitch, thus the transmission path for the survival sensor node can be obtained again, and theinfluence of the invalid point to the sensor network signals are minimize to the smallest.Meanwhile, depending on the related reliability theory, on the basis of model reconstruction, thereliability of traditional and introducing optical switch FBG sensor network are compared andanalyzed separately. The research results indicate that the reliability of the introducing opticalswitch FBG sensor topology is higher than the traditional one obviously. When the failure rate ofthe single component is diffeent, the reliability of the two network topology is also different.When the single component failure rate change between0.001and0.01, if the ratio that thedistance error between the actual value and predicting value less than or equal to40mm countsdivide total sample counts reachs to95%, the failure rate of the introducing optical switch sensornetwork topology is reduced to50%than the traditional one. If the ratio that the distance error between the actual value and predicting value less than or equal to40mm counts divide totalsample counts reachs to90%, the failure rate of the introducing optical switch sensor network isreduced to12.5%at least than the traditional one.
Finally, the high reliability FBG sensor network based on multi-agent technique is researched.Not only the multi-agent framework of the structure health monitoring system based on the FBGsensor network is designed, but also the agent’s structure and function, communication mode anddata fusion method between agents are designed, together with the static load on the plane wingbox test pannel as subject，the double agent cooperation is studied. In the first place, depending onthe sensor network optimization method above proposed, the arrangement position and number ofthe FBG sensor network adhered to the structure is determined. Following, the agent fusion resultthat each FBG sensor is in good condition in the agent and partial sensors can’t work as normal orsome sensors signal can’t be acquired in one agent is demonstrated separately. If all of the FBGsensor signals can be acquired correctly in each agent, the weighted average data fusion method isused to fuse the predicting result of the two agents directly for each external loading damageposition. If partial sensor signals can’t be acquired in one agent, at first, the model reconstructionalgorithm above proposed is used to compensate the invalid sensor signals in the faulted agent, onthat basis, the weighted average data fusion method is used to fuse the predicting result of the twoagents for each external loading damage, and the final predicting results are obtained for thestructural health monitoring. The research results indicate that the predicting accuracy of theexternal loading position is not only improved by the multi-agent technique, but also the reliabilityof the structural health monitoring system if partial sensors are invalid in the network is improved.
引文
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