Knowledge based technique to enhance the performance of neural network based motor fault detectors

The monitoring and fault detection of motors is a very important and difficult topic. Neural networks can often be trained to recognize motor faults by examining the performance of certain motor measurements. Unfortunately, several weaknesses exist for neural networks when used in this application. Examples of these shortcomings are that they can take a considerable time to train, often have less than desirable accuracy and generally are very dependent on the choice of training data. Although neural networks can recognize the nonlinear relationships that exist between motor measurements and motor faults, all aspects of the neural network fault detector performance can be improved if appropriate heuristics can be used to preprocess the input-output training relationship. This paper presents a novel approach of applying knowledge based modeling techniques to preprocess the training data and significantly improve the overall performance of the neural network based motor fault detector