Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Abstract

The challenges associated with diagnosing faults in three-phase induction motors necessitate the development of innovative, non-invasive methods that can increase efficiency and reduce costs. This study presents a novel approach to fault detection in these motors, leveraging advanced machine learning technology. The primary focus is the identification of faults related to the stator, including single-phase and three-phase faults, current interruptions, and sudden torque changes. Convolutional Neural Networks (CNN), inspired by the human visual nervous system, form the backbone of the proposed fault detection methodology. This technique utilizes external measurements for processing, circumventing the need for intrusive measures such as opening the motor or installing internal sensors. The non-intrusive nature of this method not only simplifies the process but also significantly reduces associated costs. The CNN-based approach offers superior accuracy in diagnosing faults, facilitating timely prevention measures and potentially saving human lives. It also reduces the time and effort required to identify fault types, thus minimizing motor downtime and associated costs. Simulations were conducted using MATLAB software, and individual fault scenarios were applied and analyzed. The results obtained demonstrate the efficacy of the CNN-based fault diagnosis method, thereby highlighting its potential for implementation in real-world scenarios. This study contributes to the field by providing a detailed exploration of a non-invasive, cost-effective, and highly accurate method for fault detection in three-phase induction motors. It opens avenues for further research into the application of machine learning techniques for fault diagnosis in other types of motors.