Self-reorganization method of symptom parameters for failure diagnosis by genetic algorithms

In the field of failure diagnosis of plant rotating machinery, one of the most important and most difficult things is the identification of symptom parameters (SP). By using the optimum SP, failures can be sensitively detected and the failure types can be distinguished. However, there is no acceptable method for extracting the optimum SP. In order to overcome this difficulty and insure highly accurate failure diagnosis, in this paper, a new method called “self-reorganization of symptom parameters” has been proposed by using genetic algorithms (GA). And the new method can also be applied to other pattern recognition problems. By applying the method to many practices, the optimum SP can be quickly discovered. Several examples show that this method is very effective