Fault Diagnosis of an Industrial Plant at Different Operating Points Using Neural Networks

Abstract

Industrial plants often work at different operating points. However, in literature applications of neural networks for fault detection and 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 work addresses the design of a single neural network for the diagnosis of abrupt fault (bias) in the sensors of a single-shaft industrial gas turbine working at different conditions. Data pre-processing methods are investigated to enhance fault classification, to reduce the complexity of the neural network and to facilitate the learning procedure. Results illustrating the performance of the trained neural network for sensor faults diagnosis and using simulated and real industrial data are finally shown.