Fault diagnostic system for predictive maintenance on a Brayton cycle power plant

Abstract

Model-based fault detection and diagnostic systems have become an important solution (Munoz & Sanz-Bobi, 1998:178) in the industry for preventive maintenance. This not only increases plant safety, but also reduces down time and financial losses. This paper investigates a model-based fault detection and diagnostic system by using neural networks. To mimic process models, a normal feed-forward neural network with time delays is implemented by using the MATLAB@ neural network toolbox. By using these neural network models, residuals are generated. These residuals are then classified by using other neural networks. The main process in question is the Brayton cycle thermal process used in the pebble bed modular reactor. Flownet simulation software is used to generate the data, where practical data is absent. Various training algorithms were implemented and tested during the investigation of modelling and classification concepts on two benchmark processes. The training algorithm that performed best was finally implemented in an integrated concept.