Fault diagnosis of a nuclear processing plant at different operating points using neural networks

The process under investigation in this work is the integrated dry route (IDR) process of British Nuclear Fuels plc. (BNFL), which is a nuclear fuel processing plant, where non-catastrophic faults are known to occur and a reliable early fault diagnosis scheme was required for operator advice. This paper describes the application of artificial neural network techniques to the diagnosis of non-catastrophic faults in the IDR process which operates at a few different operating points. The techniques involved developing methods to preprocess the data by statistical scaling, reducing the neural network input space using principal component analysis and training and testing the neural networks. Results are presented to illustrate the performance of the developed scheme on application to the IDR process data