Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design...Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.展开更多
电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平...电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平台对各阶段研究成果进行集成和性能提升。数字孪生(digital twin,DT)技术加强了对变压器多物理部件运行参数的监测和集成,通过在虚拟空间多物理多尺度建模实现对变压器综合故障分析,是变压器PHM演变的重要方向。鉴于此,对面向电力变压器PHM的DT技术进行了研究,阐述了变压器PHM内涵,归纳了面向变压器PHM的DT技术框架、关键技术、面临的挑战及未来发展趋势等,分析了DT与支撑其的数据、模型、计算等之间的关系,旨在为变压器运维领域数字孪生技术研究人员提供参考。展开更多
基金the National Natural Science Foundation of China(No.51175502)
文摘Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.
文摘电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平台对各阶段研究成果进行集成和性能提升。数字孪生(digital twin,DT)技术加强了对变压器多物理部件运行参数的监测和集成,通过在虚拟空间多物理多尺度建模实现对变压器综合故障分析,是变压器PHM演变的重要方向。鉴于此,对面向电力变压器PHM的DT技术进行了研究,阐述了变压器PHM内涵,归纳了面向变压器PHM的DT技术框架、关键技术、面临的挑战及未来发展趋势等,分析了DT与支撑其的数据、模型、计算等之间的关系,旨在为变压器运维领域数字孪生技术研究人员提供参考。
