Neural networks-based scheme for fault diagnosis in fossil electric power plants

This paper presents the development and application of a neural networks-based scheme for fault diagnosis in fossil electric power plants. The scheme is constituted by two components: residuals generation and fault classification. The first component generates residuals via the difference between measurements coming from the plant and a neural network predictor. The neural network predictor is trained with healthy data collected from a full scale simulator reproducing reliably the process behavior. For the second, component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. The fault patterns are stored in an associative memory based on a recurrent neural network. The scheme is applied off-line employing data bases obtained from the simulator.