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Application of Neuro-fuzzy Network for Fault Diagnosis in an Industrial Process

The purpose of this paper is to present results that were obtained in fault diagnosis of an industrial process. The diagnosis algorithm combines an artificial neural network (ANN) based supplement of a fuzzy system in a block-oriented configuration. A methodology for designing the system is described. As a motivating example, a chemical plant with a recycle stream is considered. Faults in the supply of raw materials and in controllers are simulated. The performance of the system in handling simultaneous faults is also analysed. The running test results show that the strategy appears to be better suited to diagnose faults of such an industrial process.