Incipient fault diagnosis in electrical drives by tuned neural networks

In order to identify any decrease in efficiency and any loss in industrial application a suitable monitoring system for processes is often required. With the proposed approach useful diagnostic indications can be obtained by a low-cost extension of the monitoring activity. In this way, the reliability of the obtained indications can be significantly increased considering the combination of advanced timefrequency transform, or time - scale, such as wavelets, and a new evolutionary optimisation approach based on Artificial Neural Networks (ANNs). This paper describes an approach to the joint optimization of neural network structure and weights which can take advantage of the backpropagation algorithm as a specialized decoder. The presented approach has been successfully applied to a real-world machine fault diagnosis problem.