University of Ottawa constant and variable speed electric motor vibration and acoustic fault signature dataset

Induction motors are used in industry as they are self-starting, reliable, and affordable. Applications for these motors include lathes, mills, pumps, power conveyor belts, and commercial electrical and hybrid vehicles. Induction motors have various types of failures, including rotor unbalance, rotor misalignment, stator winding faults, voltage unbalance, bowed rotor, broken rotor bars, and faulty bearings. There is a need for differentiating mechanical faults from electrical fault signals when identifying what part of the motor needs maintenance while using machine learning. Therefore, data collection is essential for electric motor fault diagnosis. The University of Ottawa Electric Motor Dataset - Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) is provided to address this issue. Data from accelerometers, temperature, and acoustic sensors are collected to provide quality electric motor fault data. The dataset includes various induction motor faults useful for time domain analysis. The high-quality data provided by this dataset will help facilitate the differentiation between mechanical faults and electric faults when using fault detection methods, which is a valuable asset for machine condition monitoring.