The problem of thermal fluids transfer supervision is a typical non linear process which suffers from lack of detectability when analytic model based techniques are applied on fault detection, isolation and reconfiguration tasks. This work describes the implementation of a supervision strategy to be applied on thermal fluids transfer systems for which massive neural networks based conventional functional approximation techniques associated to recursive rule based techniques on the basis of parity space approaches are applied. Results shown that diagnosis applied to fluid transfer problems can be carried out under acceptable determinism and reliability.
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