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Fault detection using neuro-fuzzy networks

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Kowal M. , Korbicz J. , Mendes M. J. , Calado J. M.

Systems Science

2002

tom Vol. 28, nr 1

45-57

Generally, there methodologies for developing and testing fault detection (FD) algorithms can be distinguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench that consists of process under supervision (two interconnected stations), supervision unit, fault diagnosis unit and fault simulation unit. All elements of the bench are connected to a PROFIBUS network that acts as the communication system exchanging information between automation system and distributed field devices. A realistic and flexible environment for developing and testing FD systems has been constructed using elements commonly used in industry. During the current studies actuator faults, sensor faults and leakages have been considered as incipient and abrupt faults. The proposed FD algorithm is based on neuro-fuzzy models that are responsible for residual generation.