Existing and new generation of nuclear power plants have economic and reliability concerns as addressed by overall plant performance, unscheduled downtime and the long-term management of critical assets. The key to achieving these needs is to develop an integrated approach for monitoring, control, fault detection and diagnosis of plant components such as sensors, actuators, control devices and other equipment. Both single and multiple fault cases have been considered. This paper presents the following approach for achieving this goal: 1. Development of data-driven system models using Group Method of Data Handling (GMDH), Principal Component Analysis (PCA) and Adaptive Network-based Fuzzy Inference System (ANFIS), 2. Fault detection by tracking model residuals of selected process variables and control functions, and 3. Fault isolation using a rule-based technique and/or a pattern classification technique. This approach is illustrated for a nuclear plant steam generator. Fault detection and isolation (FDI) of sensors and field devices is an important step towards the implementation of an automated and intelligent process control strategy, especially for Generation-IV reactors. (C) 2003 Elsevier Science Ltd. All rights reserved.
