Partial syndrome-based system-level fault diagnosis using game theory

Elhadef, Mourad; Grira, Sofiane

doi:10.1080/17445760.2017.1284213

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Fault-tolerant control refers to the control technology that when the control system fails, its controller can compensate for the negative impact of the failure on the system in a timely and effective manner, thereby ensuring that the control system can maintain stability and specific main functions [15][16]. Early fault control techniques used a hardware redundancy design approach, which added a "spare" component that could fail to the system, but this approach was expensive, bulky, heavy equipment, and required operators with extensive experience and knowledge.…”

Section: Fault-tolerant Controlmentioning

confidence: 99%

Fault Tolerant Control of Distributed System Based on Neural Network

2020

DPS

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Neural networks are very promising in the design and control of distributed systems due to their powerful computing power, learning and adaptive capabilities, and distributed capabilities. The purpose of this work is to study fault-tolerant control of distributed systems based on neural networks. For nonlinear systems with actuator failures, fault-tolerant control based on a small portion of the network is required. By combining error tracking and forecasting methods to build performance metrics and reduced fault-tolerant systems, this translates into a control problem. The PSO algorithm is introduced to train the judgment neural network, which avoids the selection of the initial vector and improves the training success rate to a certain extent. To mitigate the effects of actuator errors, a neural network error observer is designed to estimate and compensate for errors. Finally, the effectiveness of the proposed fault-tolerant supervisory control method is proved by an example simulation.

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Section: Fault-tolerant Controlmentioning

confidence: 99%

Fault Tolerant Control of Distributed System Based on Neural Network

2020

DPS

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Sensor fault detection and isolation: a game theoretic approach

Habibi

Howard

Habibi

2018

International Journal of Systems Science

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This paper studies sensor fault detection using a game theoretic approach. Sensor fault detection is considered as change point analysis in the coefficients of a regression model. A new method for detecting faults, referred to as two-way fault detection, is introduced which defines a game between two players, i.e. the fault detectors. In this new strategic environment, assuming that the independent states of the regression model are known, the test statistics are derived and their finite sample distributions under the null hypothesis of no change are derived. These test statistics are useful for testing the fault existence, as well as, the pure and mixed Nash equilibriums are derived for at-most-one-change and epidemic change models. A differential game is also proposed and solved using the Pontryagin maximum principle. This solution is useful for studying the fault detection problem in unknown state cases. Kalman filter and linear matrix inequality methods are used in finding the Nash equilibrium for the case of unknown states. Illustrative examples are presented to show the existence of the Nash equilibriums. Also, the proposed fault detection scheme is numerically evaluated via its application on a practical system and its performance is compared with the cumulative sum method.

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A Fault Prediction Algorithm Based on Rough Sets and Back Propagation Neural Network for Vehicular Networks

Geng

Wang

et al. 2018

IEEE Access

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Partial syndrome-based system-level fault diagnosis using game theory

Cited by 4 publications

References 19 publications

Fault Tolerant Control of Distributed System Based on Neural Network

Fault Tolerant Control of Distributed System Based on Neural Network

Sensor fault detection and isolation: a game theoretic approach

A Fault Prediction Algorithm Based on Rough Sets and Back Propagation Neural Network for Vehicular Networks

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