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- Fault isolation based on HSFNN applied to DAMADICS benchmark problemPublication . Calado, João Manuel Ferreira; Louro, R.; Mendes, Mário J. G. C.; Costa, J. M. G. Sá da; Kowal, M.The present paper is concemed with the application of a hierarchical structure of fuzzy newal networks (HSFNN) to fault isolation on a pneumatic servo-motor actuated valve that is the benchmark considered for all the DAMADICS (Development and Application of Methods for Actuator Diagnosis in IndusIrial Control Systems) project partners. The adoption of a hierarchical structure of fuzzy newal netwoIks for fault isolation pwposes aims the development of an architecture that can localise abrupt and incipient single and multiple faults correctly or at least with a minimum misclassification rate and be easily trained, ftom only single abrupt fault symptoms.
- 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.
- Neuro and neuro-fuzzy hierarchical structures comparison in FDI: a case studyPublication . Calado, João Manuel Ferreira; Mendes, Mário J. G. C.; Costa, J. M. G. Sá da; Korbicz, JózefIn this paper a hierarchical structure of several artificial neural networks has been developed for fault isolation purposes. Two different approaches have been considered. The hierarchical structure is the same for both approaches, but one uses multi-layer feedforward artificial neural networks and the other uses fuzzy neural networks. A result comparison between the two architectures will be presented. It is aimed to isolate multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. A continuous binary distillation column has been used as test bed of the current approaches.