1993 Fiscal Year Final Research Report Summary
Safety Assessment of Autonomous Systems
| Project/Area Number |
04832025
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
社会システム工学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
KOHDA Takehisa Kyoto Univ., Aeronautical Eng., Assoc.Professor, 工学部, 助教授 (60205333)
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| Co-Investigator(Kenkyū-buntansha) |
INOUE Koichi Kyoto Univ., Aeronautical Eng., Professor, 工学部, 教授 (70026079)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Autonomous System / Safety Assessment / Bond Graph / Accident Sequence Scenario / Causal Relation / Energy Flow / Qualitative Reasoning / System Failure Cause |
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| Research Abstract |
At the first stage of the project, a bond graph approach is developed for obtaining system failure causes in automatically-ontrolled plants. Since bond graphs show a direct correspondence between their component and physicai phenomenon, it is easy to obtain not only a system model, but also component failure representations. Using the stepwise refinement of the bond graph, the causal relations can be analyzed qualitatively or quantitatively depending on the available information on the system. The system failure can be defined as a deviation of a specific process variable, and its causes can be derived by tracing back the causal relations obtained by the formal causality assignment procedure in the bond graph. The effect of a component failure can be also analyzed.
At the second stage of the project, we consider how to obtain the system failure causes in autonomous systems. Since software and hardware components of the autonomous system can be represented in the form of if-then rules, the possible causes can be identified by tracing their causality. However, their effect may be nullifiled by the protective action taken in the system. The effect of a possible system failure cause must be evaluated. Since the evaluation of the related control loops is almost impossible for all disturbance situations beforehand, the method ask the system analyst about necessary malfunction conditions for its occurrence every time when a possible system failure cause is obtained. In this way, an interactive method is developed to obtain system failure causes. Further, based on the obtained relations between possible system failure causes and their related inspection variables, diagnosis rules of autonomous systems are developed. A risk-based approach is proposed not only in the failure diagnosis under uncertain conditions where several abnormal events are possible, but also in construction of a diagnosis tree.
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