MOTOR FAULT DETECTION DATA

Induction motors play a crucial role in industrial applications, but their operation is often compromised by various mechanical and electrical faults. This paper presents a new dataset for comprehensive fault diagnosis of three-phase induction motors, using synchronized multi-sensor data collection. The dataset includes real-time measurements of vibration, voltage, and current collected from a 0.2 kW squirrel cage induction motor. Fault scenarios such as phase removal and mechanical misalignments were simulated to capture a wide range of motor behaviors. Data were collected using high-resolution sensors, with the vibration ,voltage and current sampled at 50kHz. The dataset is organized into tem distinct CSV files, covering different operational scenarios, providing a comprehensive resource for researchers aiming to develop or test fault detection algorithms. The dataset was used to train a RandomForest classifier for fault detection, achieving an accuracy of 99.82%. This demonstrates the effectiveness of the dataset for developing machine learning models aimed at real-time fault diagnosis and predictive maintenance. Unlike existing datasets, this collection provides synchronized data across multiple sensor types, enabling cross-analysis of electrical and mechanical faults. The dataset is publicly available, offering a valuable tool for advancing research in motor fault diagnosis and predictive maintenance.