Abstract
A strategy based on rough set (RS) and artificial neural network (ANN) is developed to detect and diagnose sensor drifting faults. The reduced information is used to develop classification rules and train the neural network to infer appropriate parameters. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for sensor fault detection and diagnosis. Simultaneous temperature sensor drifting faults of the supply chilled water (SCW) and return chilled water (RCW) can be diagnosis successfully.
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