Detection, localization, and identification of bearing faults on an ocean turbine

Abstract

A real-time, vibrations-based condition monitoring method used to detect, localize, and identify a faulty bearing in an ocean turbine electric motor is presented in this paper. The electric motor is installed in a dynamometer emulating the functions of the actual ocean turbine. High frequency modal analysis and power trending are combined to assess the operational health of the dynamometer's bearings across an array of accelerometers. Once a defect has been detected, envelope analysis is used to identify the exact bearing containing the defect. After a brief background on bearing fault detection, this paper introduces a simplified mathematical model of the bearing fault, followed with the signal processing approach used to detect, locate and identify the fault. Lastly, the approach is illustrated through the analysis of a series of experimental data collected over the course of a month leading up to a fault in the dynamometer. By retroactively trending the data leading to the near-failure of one of the electric motors in the dynamometer, the authors identified a positive trend in energy levels for a specific frequency band present across the array of accelerometers and to identify two bearings as possible sources of the fault.