An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults

Gears are used for the transfer of mechanical power and are an important part of the electromechanical transmission system. Unexpected failure of gear could cause shutdown of the machines and proves to be expensive in terms of production loss and maintenance. Therefore, reliable condition monitoring is required to protect unexpected gear failures. It has been highlighted in the recently published literature that the gear faults appear at the specific gear frequencies in the instantaneous power spectrum of the motor. However, the amplitudes of these gear frequencies are very small and are shadowed by the environment noise. Thus, reliable diagnosis of gear faults is a challenge in real-time fault diagnosis systems. This issue has been addressed in this paper through the development of the automated spectral extraction algorithm. The theoretical investigation has been verified through the custom-designed experimental test rig.