Assessment of Three-Phase induction Motor Voltages Using Smartphone Acoustic Data

Abstract

Three-phase induction motors(TIMs) are the main source of mechanical energy in industries. Due to the advent of voltage inverters and advances in power electronics, these devices can be operated over a wide range of speeds. However, TIMs are often exposed to failures, which require different types of maintenance. When an industrial process is interrupted by a motor failure, there can be significant financial losses. Therefore, researchers seek to develop sensor-based approaches to detect mechanical and electrical faults to avoid losses and damages. Different methods have been proposed. However, the acoustic emission (AE) method attacks attention due to new affordable sensors and easy application. Also, one of the most common electrical faults in TIMs is unbalanced voltages. It causes torque losses, overcurrents, and excessive vibration. Currently, there are already several motors being monitored by AE equipment. However, unbalanced voltages are not considered by any of them, which can lead to false diagnoses. Therefore, this work focuses on developing a new method able to detect, identify the affected phase, and classify the magnitude of unbalanced voltages in TIMs. Additionally, a new signal processing technique was proposed, which led to a new index to mitigate the high acoustic noise found in industrial plants. Several experiments were carried out, and the results showed the efficiency of the novel technique in performing the detection, phase identification, and magnitude classification of unbalanced voltages in induction motors.