Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. For critical equipment, such as drilling equipment, top drives, mining equipment, wind turbine main rotors, helicopter swash plates, etc. this poses safety and logistics support problems. An undetected bearing fault can disrupt service, and causes delays, lost productivity, or accidents. This paper examines a strategy for analysis of large slow bearings to improve the fault detection of condition monitoring systems, thus reducing operations and maintenance cost associated with these bearing faults. This analysis was based on vibration, temperature and grease analysis from three wind turbines, where one turbine was suspected of having a faulted main bearing.
How to Cite
vibration, bearing diagnostics, temperature, Grease Analysis
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