Multi-sensor fault tolerant measurement based on Tagaki-Sugeno fuzzy model
A multiple fault tolerant measurement system based on nonlinear dynamic models, special searching algorithm, principle components decomposition and Q test is developed. The proposed system uses a model-based estimator to deliver symptoms. The symptoms are then analyzed in a statistical unit in order to detect the faults and isolate the faulty sensors. Multi-layer perceptron networks, radial basis function networks and Tagaki-Sugeno fuzzy models were examined for the fault estimator module and among these fuzzy models presented the best performance. The main advantages of the proposed scheme are the capability to detect, isolate and repair multiple faults in both input and output sensors and the feasibility to be applied to any system with as many sensors as required, all due to particular design of its model-based estimator. The system was tested on a CSTH model developed based on an experimental platform; different experiments demonstrated satisfactory results.
fuzzy model multiple faults neural network fault detection fault isolation