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- Fault isolation approach using a PROFIBUS network: a case studyPublication . Mendes, Mário J. G. C.; Kowal, Marek; Calado, João Manuel Ferreira; Korbicz, Józef; Costa, J. M. G. Sá daThis paper presents the second stage, of a two-stage neuro-fuzzy system, used for fault isolation (FI) in dynamic processes and it`s built using a hierarchical structure of fuzzy neural networks. The current approach is tested under a hardware bench constructed with componentes commonly used in the industry and consists on a pilot plant under supervision, a supervision unit, a fault detection and isolation unit and a fault simulation unit. All the elements are connected to a PROFIBUS network, which acts as the communication system for exchanging information between the automation system and the distributed field devices.
- Industrial actuator diagnosis using hierarchical fuzzy neural networksPublication . Mendes, Mário J. G. C.; Calado, João Manuel Ferreira; Sousa, J. M. C.; Costa, J. M. G. Sá daIn this paper a hierarchical structure offuzzy neural networks (FNNs) and how to train it for fault isolation given an appropriate data patterns, are presented. Fault symptoms concerning multiple simultaneous faults are harder to learn than those associated with single faults. Furthermore, the larger the set of faults, the larger the set of fault symptoms will be and, hence, the longer and less certain the training outcome. In order to overcome this problem, the proposed approach has a hierarchical structure of three levels where several FNNs are used. Thus, a large number ofpatterns are divided into many smaller subsets so that the classification can be carried out more efficiently. One ofthe advantages of this approach is that multiple faults can be detected in new data even ifthe network is trained only with datarepresenting single abrupt faults. A continuous binary distillation column having several actuated valves with PID loops has been used as testbed for the proposed approach.
- Pruning algorithm applied to a hierarchical structure of fuzzy neural networks: case studyPublication . Mendes, Mário J. G. C.; Calado, João Manuel Ferreira; Costa, J. M. G. Sá daThis research paper is concerned with the fault detection and isolation (FDI) problem, or more exactly, with a hierarchical structure of fuzzy neural networks (HFNN) used for fault isolation purposes in industrial processes. The main aim of this research work is to optimise the number of neurons in the hidden layer of all fuzzy neural networks (FNNs) used in the HFNN. Thus, the optimal brain surgeon (OBS) pruning algorithm has been used to prune all FNNs. After the OBS optimisation, the HFNN structure continues to be able to isolate correctly, abrupt and incipient, single and multiple faults. At the same time, the structure became simpler and better generalisation capabilities have been observed. A continuous binary distillation column having several actuated valves with PID control loops has been used as test bed of the proposed approach.
- Fault diagnosis system based in agentsPublication . Mendes, Mário J. G. C.; Calado, João Manuel Ferreira; Costa, J. M. G Sá daIn this work is proposed a new agent based fault diagnosis system for complex and dynamic processes. The fault diagnosis systems of the future should have present the distribution and complexity of the processes and they must be able to cooperate and communicate with other systems to achieved a satisfactory performance. The fault detection and isolation (FDI) agents proposed here have hybrid architectures based in a horizontal layered architecture. The reactive layer of the FDI agents are based in decomposition wavelet methods for the fault detection and in neural networks for the fault isolation task. The new agent based FDI system is applied to fault diagnosis in a three tank process.