Techniques for large, slow bearing fault detection

Abstract

Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. This poses a critical problem for high value assets, such as drilling equipment top drives, mining equipment, wind turbine main rotors, and helicopter swash plates. An undetected bearing fault can disrupt service, and cause 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. This helps reduce operations and maintenance cost associated with these bearing faults. This analysis is primarily concerned with vibration, and is compared to temperature and grease analysis. Data was available from three wind turbines, where one of the turbine was suspected of having a faulted main bearing.