A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
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| Affiliation: | 1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia;2. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia;3. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia;1. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;2. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;3. Faculty of Engineering, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada;1. Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan;2. College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China;1. College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, UK;2. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;3. King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Abstract: | In this paper, a hybrid neural network model, based on the integration of fuzzy ARTMAP (FAM) and the rectangular basis function network (RecBFN), which is capable of learning and revealing fuzzy rules is proposed. The hybrid network is able to classify data samples incrementally and, at the same time, to extract rules directly from the network weights for justifying its predictions. With regards to process systems engineering, the proposed network is applied to a fault detection and diagnosis task in a power generation station. Specifically, the efficiency of the network in monitoring the operating conditions of a circulating water (CW) system is evaluated by using a set of real sensor measurements collected from the power station. The rules extracted are analyzed, discussed, and compared with those from a rule extraction method of FAM. From the comparison results, it is observed that the proposed network is able to extract more meaningful rules with a lower degree of rule redundancy and higher interpretability within the neural network framework. The extracted rules are also in agreement with experts’ opinions for maintaining the CW system in the power generation plant. |
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