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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. In the results section, effectiveness of the methods of bearing fault detection presented in this paper is demonstrated through processing data collected, first, from a controlled lathe setup and, second, from the dynamometer. By mounting a bearing containing a defect punched into its inner raceway to a lathe and placing an array of accelerometers along the length of lathe, the bearing fault is clearly detected, localized, and identified as an inner raceway defect. Through 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 identify two bearings as possible sources of the fault.
 defects, Bearing Faults, turbine engine, rotating machinery, modulation; demodulation, Fault Detection; Techniques; Ocean Turbine, renewable energy, ocean engineering
Highlights
The Southeast National Marine Renewable Energy Center (SNMREC) is developing an Ocean Turbine (OT) that is capable of harnessing some of the energy contained in the Florida Current (Driscoll, 2008)
This paper focuses on detecting bearing raceway faults, application of this methodology to identify a bearing cage faults or ball defects, involves calculating the Fundamental Train Frequency (FTF) or Ball Spin Frequency (BSF) and comparing the impact frequency extracted from the analytic signal to these two values
In the first set of experimental results, effectiveness of the methods outlined in this thesis are demonstrated by detecting, localizing, and identifying a bearing with a raceway defect punched into its inner raceway
Summary
The Southeast National Marine Renewable Energy Center (SNMREC) is developing an Ocean Turbine (OT) that is capable of harnessing some of the energy contained in the Florida Current (Driscoll, 2008). By creating a self-diagnostic program capable of identifying the occurrence of a fault, but evaluating its severity and localizing it, said maintenance costs could be significantly reduced. Through vibrational analysis, which has been used to effectively detect and diagnose faults within other rotating machinery, one can achieve a level of awareness associated with the type of fault occurring, its location, and a relative level of severity. This information can be used to determine whether maintenance is pertinent to prevent further damage to the system (Jayaswal, Wadhwani, & Malchandani, 2008)
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