Fault detection and isolation in technical processes with neural networks

In this paper a neural network based fault detection and isolation concept for supervision of technical processes is introduced. Three different possibilities to employ neural networks for fault diagnosis are discussed. In most existing schemes the steps of residual generation and residual evaluation are performed on the basis of analytical process knowledge. Here neural networks are proposed for these tasks. First of all a neural network can be used instead of a mathematical model for residual generation, secondly another neural network can be trained to perform the classification task for residual evaluation and therefore fault isolation. A third possibility is a one-step diagnosis (OSD), where one neural network is trained to directly detect and isolate possible faults from the available measurements without the need for prior generation of intermediate signals as residuals. Results from the application of a restricted Coulomb energy neural network (RCE) to the residual evaluation and alternatively to the one-step diagnosis at an industrial actuator benchmark problem are presented