Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Modelling and method for beef quality risk identification and optimization in beef cattle breeding
WSEAS Transactions on Information Science and Applications
Design of fault tolerant flight control system
WSEAS Transactions on Systems and Control
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In this paper, we propose to design a fault detection filter for a linear time invariant system using the non-dominated sorting genetic algorithm II. The fault detection filter is an observer with a set of projectors that map each fault in a specific residual direction. The design of the fault detection filter is formulated as a multiobjectives optimisation problem in the frequency domain. The non-dominated sorting genetic algorithm II is utilised to tune the filter gain and each projector in order to minimise the sensitivity of the fault signals to be blocked and maximise the sensitivity of each fault signal to be identified in each residual direction. With this approach, different fault isolation problems can be formulated; simultaneous faults or one fault at a time. Furthermore, there is a large freedom in the way the observer gain and the projectors can be designed. Finally, the viability of the approach is demonstrated through the detection and the isolation of sensor and actuator faults for a linear aircraft model.