Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection

Induction motors are critical to industrial operations but are prone to mechanical and electrical faults. This paper introduces a new dataset for comprehensive fault diagnosis of three-phase induction motors, featuring synchronized multi-sensor data collection. Real-time measurements of vibration, voltage, and current were captured from a 0.2 kW squirrel cage induction motor using high-resolution sensors, with all signals sampled at 50 kHz. Fault scenarios, including phase removal and mechanical misalignments, were simulated to capture diverse motor behaviors. The dataset, organized into ten distinct CSV files covering various operational states, provides a rich resource for developing and testing fault detection algorithms. A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. Unlike existing datasets, this collection offers synchronized electrical and mechanical sensor data, enabling advanced cross-sensor fault analysis. The dataset is publicly available and aims to support researchers in advancing machine learning approaches for motor health monitoring.