APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROCESS FAULT DIAGNOSIS

Chemical processes are systems that include complicated network of material,energy and process flow. As time passes, the performance of chemical processgradually degrades due to the deterioration of process equipments andcomponents. The early detection and diagnosis of faults in chemical processesis very important both from the viewpoint of plant safety as well as reducedmanufacturing costs. The conventional way used in fault detection anddiagnosis is through the use of models of the process, which is not easy to beachieved in many cases. In recent years, an artificial intelligence technique suchas neural network has been successfully used for pattern recognition and assuch it can be suitable for use in fault diagnosis of processes [1]. Theapplication of neural network methods in process fault detection and diagnosisis demonstrated in this work in two case studies using simulated chemical plantsystems. Both systems were successfully diagnosed of the faults introduced inthem. The neural networks were able to generalise to successfully diagnosedfault combinations it was not explicitly trained upon. Thus, neural network canbe fully applied in industries as it has shown several advantages over theconventional way in fault diagnosis.