Time domain averaging across all scales: A novel method for detection of gearbox faults

The vibration signal of a gearbox carries the signature of the fault in the gears. As such early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. Time synchronous averaging can extract the periodic waveforms of a noisy vibration signal, whereas Wavelet transformation is able to characterize the local features of the signal at different scales. This paper proposes a new technique, time domain averaging across all scales, which combines the time synchronous average and wavelet transformation together to extract the periodic waveforms at different scales from noisy vibration signals. The technique efficiently cleans up noise and detects both local and distributed faults simultaneously. A pilot plant case study is presented to demonstrate the efficacy of the proposed technique.