Detection of Precursors to Component Failure in a Spur Gear Drive-Train by Means of a Torque Transducer

Abstract

In the wind energy industry, gearbox failures are among the most costly and the most frequent, adding significantly to the operation and maintenance costs over the life cycle of the turbine. Despite significant improvements in the understanding of gear loads and dynamics, even to the point of establishing international standards for design and specifications of wind turbine gearboxes, these components generally fall short of reaching their 20 year design life. In a significant number of gearbox failures, the primary bearing on the low speed shaft experiences faults in its operation, such as misalignment and movement on the mounts. To investigate the topic of gear health management, a fault detection approach is applied to a test bed involving a spur gear double-reduction transmission, outfitted with a torque transducer and triaxial accelerometers on the bearing cases. The test bed is not a wind turbine gearbox – the gear arrangement is different and the gears are smaller compared to that of a typical wind turbine gearbox – but it does serve to test the modeling and fault detection methods proposed in this paper. Both baseline and faulted measurements are taken from the experimental set-up for data analysis. It is shown that the torque sensor provides an early indication of fault precursors, such as misalignment and decreased lubrication, while also maintaining the capacity to identify mature faults, such as chipped and missing gear teeth. The measurements are analyzed using statistical based methods – the Mahalanobis distance and Parzen discriminant analysis. These features for fault detection are then characterized at various operating speeds for each of the geartrain conditions of interest. An analytical model is created from first principles for verification of results and for simulation of the free and forced dynamics of the system.