The design of accurate fault diagnosing systems aides process safety and also improves product quality. Therefore, in recent years, many researchers have conducted work on this topic and suggested various techniques for fault detection and diagnosis. In this paper, the fuzzy system based on knowledge of the process has been proposed for fault detection and diagnosis. In order to improve the performance of the diagnoser system, the genetic algorithm has been used to optimize membership function parameters of fuzzy diagnosis system. To test the performance of the proposed diagnose system, toluene production plant was used. The simulation results indicate that the proposed algorithm is capable of diagnosing various combinations (from two to four simultaneous faults) of simultaneous faults.
Tayyebi, S. (2015). Multiple-Fault Diagnosing System Based on Fuzzy-Genetic Algorithm for the Production of Toluene from n-Heptane. Farayandno, 9(48), 44-52.
MLA
Shokoufe Tayyebi. "Multiple-Fault Diagnosing System Based on Fuzzy-Genetic Algorithm for the Production of Toluene from n-Heptane". Farayandno, 9, 48, 2015, 44-52.
HARVARD
Tayyebi, S. (2015). 'Multiple-Fault Diagnosing System Based on Fuzzy-Genetic Algorithm for the Production of Toluene from n-Heptane', Farayandno, 9(48), pp. 44-52.
VANCOUVER
Tayyebi, S. Multiple-Fault Diagnosing System Based on Fuzzy-Genetic Algorithm for the Production of Toluene from n-Heptane. Farayandno, 2015; 9(48): 44-52.
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