Um estudo sobre multissensoriamento no diagnóstico de falhas em motores de indução trifásicos

Abstract

Three-phase induction motors are the most used electrical machines in the industrial sector because they have both constructive and economic characteristics that make their use advantageous. It is observed that predictive maintenance plays a prominent role in industrial maintenance routines. Thus, the diagnosis of faults in three-phase induction motors, as well as the classification of these faults, is a broad area of study that involves the study of the types of faults, the sensors, the application of signal processing techniques and classification of patterns of the data. Given the great relevance of the topic related to electrical machine failures, this work creates a database of failures in three-phase induction motors from data from current, vibration, audio, voltage, magnetic flux, torque and speed sensors. Still, a study is carried out that allows the validation of the collected data, verifying if they have the characteristics to be used in the identification and classification of faults in electric motors. It was observed that all sensors used can provide useful information for the identification and classification of failures in electric motors, and that there are selected attributes in common between the two motors used in this work. The database created will be an important tool for the development of studies related to failures in electrical machines.