Neural networks for fault diagnosis of industrial plants at different working points

Industrial plants often work at different operating points. However, in literature applications of neural networks for fault diagnosis usually consider only a single working condition or small changes of operating points. A standard scheme for the design of neural networks for fault diagnosis at all operating points may be impractical due to the unavailability of suitable training data for all working conditions. This paper addresses the design of a single neural network for the diagnosis of faults in the sensors of an industrial gas turbine working at different conditions. The presented results illustrate the performance of the trained neural network for sensor fault diagnosis.