Neural Network Models and Statistical Tests as Flexible Base for Intelligent Fault Diagnosis

Abstract

The increasing complexity of industrial control systems and industrial processes makes it necessary to frequent use tools and techniques for reliability and safety analysis, particularly to early detection and localization of faults. By applying conventional approaches to diagnose faults via both dynamic process and signal models and parameters estimation and knowledge processing the inherent redundances can be used to make it more effective and to detect faults earlier. But in many cases, it is not enough to design the unique fault diagnosis procedure, but it is also essential to provide its flexible adaptation to changes of environment of the controlled system, including structures, parameters, tasks of the operators, their(changing) knowledge, cognitive capabilities etc. The major efforts have to be made to develop new generation of knowledge-based fault detection (FD) systems with both analytical and heuristical knowledge to be easily adapted to the changes in the system's environment. The paper de-sribes integrated approach to the "homogeneous" introducing the human-like thinking neuron schemes in all the stages of the model based fault diagnosis. Several examples are discussed.