Machine Learning for Anomaly Detection in Induction Motors

This paper proposes a fault detection system for induction motors based on vibration analysis and machine learning algorithms. Induction motors are widely used in various industrial applications and their proper functioning is crucial for the efficient operation of machines and processes. However, they are also prone to various faults and failures that can lead to unplanned downtime, production losses, and increased maintenance costs. The proposed system utilizes vibration sensors to measure the vibration signals of the motor and extract their spectral features. These features are then analyzed using various machine learning algorithms, such as artificial neural networks, support vector machines, and decision trees, to identify the patterns indicative of different types of faults. The proposed system is tested on a dataset of real-world measurements and shows promising results, demonstrating the potential of this approach for improving the maintenance of industrial equipment. The paper presents the results of experiments conducted on a set of induction motors, which demonstrate the effectiveness of the proposed system in detecting different types of faults. The system is shown to achieve high accuracy and reliability, and can provide early warning of impending failures, allowing for timely maintenance and repair.