Fuzzy-genetic algorithm for automatic fault detection in HVAC systems
Applied Soft Computing
Bearing fault detection using artificial neural networks and genetic algorithm
EURASIP Journal on Applied Signal Processing
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In this paper a multilayer Perceptron (MLP), a Probabilistic Neural Network (PNN) and a fuzzy approach are proposed in on-line sensors and actuators fault detection and isolation for systems with parameter uncertainties. The residuals obtained by analytical redundancy relations (ARRs) are used as inputs of the three systems. The MLP is trained to present for each output signal 1 in the occurrence of a fault at the associated variable and 0 otherwise, PNN has one output which is trained to present the fault index (1 in case of a fault-free context, 2 in case of a fault affecting variable 1...). These previous approaches are improved by the use of genetic algorithms (GA) to optimize the initial weights and bias in case of MLP, the spread in case of PNN and the membership functions parameters in case of the fuzzy approach. MLP, PNN and fuzzy approach are compared through a hydraulic example.