Process Fault Diagnosis and Control Using Neural Networks

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The major goal of this project is to research and demonstrate a new approach based on neural networks towards the design of process fault detection and control systems. Past approaches in fault diagnostic systems did not properly include the human expert's reasoning strategies and experience and hence were not adequate in efficient and correct trouble-shooting. This project, will explore a novel method using neural networks that address the important issues which are central to the development of knowledge-based systems for process fault diagnosis and control. Our method for developing knowledge-based systems rests on the important characteristics of neural networks, namely, their ability to automatically classify and learn associations between input and output data and to be able to handle noisy data. The P.I. has already successfully tested his approach on a prototypical case study involving a CSTR and its support subsystems. He plans to investigate this approach further by experimenting with larger prototypes of chemical process plants as well as for diagnosing dynamic process faults. He also proposes to analyze the robustness of this approach to sensor failures, noisy and uncertain data, and incomplete information on symptoms. It is also proposed to investigate the use of neural networks for modeling the behavior process units and their in control. Furthermore, he proposes to investigate the integration of neural nets with knowledge-based techniques.