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- 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.
- Neuro-fuzzy techniques in FDI system for sugar factory actuatorsPublication . Mendes, Mário J. G. C.; Kowal, Marek; Korbicz, Józef; Costa, José M. G. Sá daFault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the sugar factory actuators.
- 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.
- 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.
- “State-of-art” da detecção e diagnóstico de avarias em processos industriaisPublication . Mendes, Mário J. G. C.Ao longo das últimas décadas tem havido um interesse crescente, por parte da indústria e da comunidade académica, pelos sistemas de detecção e diagnóstico de avarias (sistemas FDI). A crescente complexidade dos processos e do seu controlo, assim como a necessidade de tornar os processos mais seguros e fiáveis incentivou a procura e desenvolvimento de sistemas FDI. O desenvolvimento e melhoria deste tipo de sistemas seguiu de perto a evolução na tecnologia, nomeadamente a evolução informática, que permitiu maior facilidade na análise de grandes quantidades de dados e aplicação de sistemas FDI em tempo real. Este artigo pretende fazer um ponto da situação, a nível mundial, desta área de investigação.
- Neuro-fuzzy structures in FDI systemsPublication . Mendes, Mário J. G. C.; Kowal, Marek; Korbicz, Józef; Costa, J. M. G. Sá daFault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy (N-F) model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the actuators of one sugar factory.
- 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.
- Neuro-fuzzy fault detection approach using a Profibus networkPublication . Calado, João Manuel Ferreira; Mendes, Mário J. G. C.; Kowal, Marek; Korbicz, Józef; Costa, J. M. G. Sá daGenerally three methodologies to develop and test FDI algorithms can de distinguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench constructed with components commonly used in industry that consists on a pilot plant under supervision, a supervision unit, a fault detection unit and a fault simulation unit. All elements are connected to a PROFIBUS network that acts as the communication system exchanging information between automation system and distributed field devices. A fault detection methodology, which is based on neuro-fuzzy models, has been developed and implemented. During the current studies actuator faults, sensor faults and leakages have been considered as incipient and abrupt faults. Several studies have also been performed under multiple simultaneous faulty scenarios.
- Fault detection using neuro-fuzzy networksPublication . Kowal, Marek; Korbicz, Józef; Mendes, Mário J. G. C.; Calado, João Manuel FerreiraGenerally three methodologies to develop and test fault detection (FD) algorithms can be distingguished: 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 exchaging information between automation system and distributed field devices. A realistic and fexible 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 incipiente and abrupt faults. The proposed FD algorithm bases on neuro-fuzzy models that are responsible for residual generation.
- Fault detection approach based on fuzzy qualitative reasoning applied to the DAMADICS benchmark problemPublication . Calado, João Manuel Ferreira; Carreira, F. P. N. F.; Mendes, Mário J. G. C.; Costa, J. M. G. Sá da; Bartys, M.A computer assisted fault detection methodology based on a fuzzy qualitative simulation algorithm is described. The adoption of fuzzy sets allows a more detailed description of physical variables, through an arbitrary, but finite, disaetisatioo of the quantity space. The fuzzy representation of qualitative values is more general than ordinary interval representation, since it can represent not only the information stated by a well defined real interval but also the knowledge embedded in the soft boundaries of the interval. Such a methodology was applied to a pneumatic servomotor actuated control valve that is the benchmark problem of the EC RTN DAMADlCS.