Computational Model for Electrical Motors Condition Analysis and Monitoring

Abstract

Predictive maintenance uses several methods to monitor the conditions of electric motors applied in industrial plants. Among these methods, vibration analysis stands out as a widely used method due to the possibility of identifying a wide chance of failures. This work presents the development of a computational model endowed with a classifying algorithm to receive vibration readings from electric motors and determine if the machine has a fault behavior and, if so, which fault. For the algorithm to detect which type of failure, a dataset with readings from several engines in different failure conditions should be developed for training the model.