Development of a dynamic maintenance system for electric motor's failure prognosis

Abstract

During the last years there have become a lot of studies concerning the preventive maintenance of mechanical equipment and fault diagnosis, each one of them having a different approach. The predictive maintenance allows to be continuously known the kind of maintenance and care that a system needs, and mainly what kind of equipment replacement is foreseen in the future. This paper presents the development of a smart- dynamic maintenance system for electric motor's failure prognosis. The method is based on the analysis of the motor into his subsystems by using neural networks and by recording their relations and interactions. In this model are also embodied the possible damages of every subsystem and its symptoms, respectively. The objective goal of this analysis is to develop an algorithm for the calculation of the probability of showing the damage in a motor's part according to its dynamic operational status. The creation of a data basis reflects the techniques experience concerning the causes and the possible preventive actions that are necessary. The suggested method is general and can be applied in every part or system of the mechanical equipment. Finally, the paper presents a simple study case for the rotor and practical conclusions are drawn which can lead to a better focus on preventive maintenance.