Nuclear power plant fault diagnosis using neural networks with error estimation by series association

The accuracy of the diagnosis obtained from a nuclear power plant fault-diagnostic advisor using neural networks is addressed in this paper in order to ensure the credibility of the diagnosis. A new error estimation scheme called error estimation by series association provides a measure of the accuracy associated with the advisor's diagnoses. This error estimation is performed by a secondary neural network that is fed both the input features for and the outputs of the advisor. Our error estimation by series association outperforms previous error estimation techniques in providing more accurate confidence information with considerably reduced computational requirements. We demonstrate the extensive usability of our method by applying it to a complicated transient recognition problem of 33 transient scenarios. The simulated transient data at different severities consists of 25 distinct transients for the Duane Arnold Energy Center nuclear power station ranging from a main steam line break to anticipated transient without scram (ATWS) conditions. The fault-diagnostic advisor system with the secondary error prediction network is tested on the transients at various severity levels and degraded noise conditions. The results show that our error estimation scheme provides a useful measure of the validity of the advisor's output or diagnosis with considerable reduction in computational requirements over previous error estimation schemes.