A heterogeneous fault diagnosis method for bearings in gearbox

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.