To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA...To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.展开更多
Recently the integrated modular avionics （IMA） architecture which introduces the concept of resource partitioning becomes popular as an alternative to the traditional federated architecture. A novel hierarchical app...Recently the integrated modular avionics （IMA） architecture which introduces the concept of resource partitioning becomes popular as an alternative to the traditional federated architecture. A novel hierarchical approach is proposed to solve the resource allocation problem for IMA systems in distributed environments. Firstly, the worst case response time of tasks with arbitrary deadlines is analyzed for the two-level scheduler. Then, the hierarchical resource allocation approach is presented in two levels. At the platform level, a task assignment algorithm based on genetic simulated annealing （GSA） is proposed to assign a set of pre-defined tasks to different processing nodes in the form of task groups, so that resources can be allocated as partitions and mapped to task groups. While yielding to all the resource con- straints, the algorithm tries to find an optimal task assignment with minimized communication costs and balanced work load. At the node level, partition parameters are optimized, so that the computational resource can be allocated further. An example is shown to illustrate the hierarchal resource allocation approach and manifest the validity. Simulation results comparing the performance of the proposed GSA with that of traditional genetic algorithms are presented in the context of task assignment in IMA systems.展开更多
Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electroni...Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.展开更多
基金supported by the National Key Research and Development Program of China(2021YFB1600601)the Joint Funds of the National Natural Science Foundation of China and the Civil Aviation Administration of China(U1933106)+2 种基金the Scientific Research Project of Tianjin Educational Committee(2019KJ134)the Natural Science Foundation of TianjinIntelligent Civil Aviation Program(21JCQNJ C00900)。
文摘To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.
基金supported by the National Natural Science Foundation of China (60879024)
文摘Recently the integrated modular avionics （IMA） architecture which introduces the concept of resource partitioning becomes popular as an alternative to the traditional federated architecture. A novel hierarchical approach is proposed to solve the resource allocation problem for IMA systems in distributed environments. Firstly, the worst case response time of tasks with arbitrary deadlines is analyzed for the two-level scheduler. Then, the hierarchical resource allocation approach is presented in two levels. At the platform level, a task assignment algorithm based on genetic simulated annealing （GSA） is proposed to assign a set of pre-defined tasks to different processing nodes in the form of task groups, so that resources can be allocated as partitions and mapped to task groups. While yielding to all the resource con- straints, the algorithm tries to find an optimal task assignment with minimized communication costs and balanced work load. At the node level, partition parameters are optimized, so that the computational resource can be allocated further. An example is shown to illustrate the hierarchal resource allocation approach and manifest the validity. Simulation results comparing the performance of the proposed GSA with that of traditional genetic algorithms are presented in the context of task assignment in IMA systems.
文摘Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.
