Early Gear Failure Detection in Fatigue Testing of Driveline Components

Abstract: Early failure detection has been an integral part of condition monitoring of critical systems, such as wind turbines and helicopter rotor drivetrains. An unexplored application of early failure detection is fatigue testing of driveline components. On many occasions, driveline components fail catastrophically, leaving no evidence of the root cause of failure and causing extensive damage to test equipment. This can be prevented by detecting failure in its early stages. Test specimen would be preserved, enabling correlation of test results with design predictions. In this thesis, a method for early failure detection of gear fatigue is proposed. The gears in questions are parts of driveline components undergoing fatigue tests. The proposed method includes generation of an autoregressive model from a healthy, time synchronously averaged vibration signal. The parameters of the generated model are then used to construct a filter, which predicts deviations from the healthy signal. The output of this filter is then processed to detect failure. Vibration data from four run to failure tests were analysed. While the proposed method detected failure in all four data sets, performance was better in tests carried out at high torque and low speed in comparison to tests carried out under low torque and high speeds. Finally, potential improvements in the proposed method to increase its effectiveness are proposed.