| Project/Area Number |
16360198
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Nagoya University |
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Principal Investigator |
SUZUKI Tatsuya Nagoya University, Graduate School of Engineering, Professor, 大学院工学研究科, 教授 (50235967)
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| Co-Investigator(Kenkyū-buntansha) |
INAGAKI Shinkichi Nagoya University, Graduate School of Engineering, Assistant Professor, 大学院工学研究科, 助手 (80362276)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥12,000,000 (Direct Cost: ¥12,000,000)
Fiscal Year 2006: ¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2005: ¥3,900,000 (Direct Cost: ¥3,900,000)
Fiscal Year 2004: ¥4,700,000 (Direct Cost: ¥4,700,000)
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| Keywords | high reliability / fault diagnosis / distributed system / hybrid dynamical system / human behavior / probabilistic model / 耐故障性 / 制御器切り替え / 制御器の切り替え / 無人宇宙往還機 / エラーリカバリ |
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| Research Abstract |
The requirements for design of control systems have been shifting from high speed and high precision to realization of high reliability and safety of the systems. Furthermore, harmonious corporation between systems and human is becoming an important factor which must be considered in the control system design. From these motivations, this research project addressed the following topics:
1. Fault detection and diagnosis based on probabilistic information theory
2. Distributed fault diagnosis focused on large scale systems
3. Modeling human behavior based on hybrid dynamical systems
In the topic 1, we proposed two new mathematical models based on probabilistic information theory. The first one is a new Hidden Markov Model (HMM) wherein a continuous dynamical model is allocated to each discrete state of HMM, and we applied the model to the fault diagnosis. The second one is a Probabilistic Production Rules wherein probabilities are assigned to the conventional production rules, and we applied
… More
the theory to the fault diagnosis of the manipulator control system. In addition, we proposed a fault diagnosis algorithm based on Timed Markov Model, and verified the availability by experiments using a prototype of automating transfer line.
In the topic 2, we proposed a new distributed diagnosis algorithm capable of diagnosing the overall system behavior by combining the local diagnosis results based on Bayesian network theory. In the proposed framework, the relationships between the faulty conditions in the subsystems and the observed signals are modeled by network structure. By applying the algorithm to a prototype of automating transfer line, we verified the availability of the algorithm.
In the topic 3, we employed two modeling approaches based on data-clustering and the new HMM developed in the topic 1. As the result, we can model human behaviors in both cases that the transition between the discrete modes is modeled deterministically and stochastically. The availabilities of these approaches were verified by applying the proposed methods to analysis of driving-behaviors and task skills.
These achievements are or will be published in scientific journals. Less
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