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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.
- 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.
- 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.