An Ensemble Dynamic Time Domain Averaging Method for Gear Fault Detection without Tachometer

Time Domain Averaging (TDA) has been widely used for fault detection. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer. Empirical mode decomposition (EMD) helps to extract physically meaningful components from the singles. Dynamic Time Warping (DTW) can solve inconsistence in signal lengths per rotation due to speed fluctuation. Utilizing the advantages of EMD, DTW and TDA, an ensemble dynamic-time domain averaging (ED-TDA) algorithm is proposed for gear fault detection without tachometer. First, the selected intrinsic mode function (IMF) and the envelop signals are equal-spaced intercepted. Then, the phase accumulation error among the envelop signal segments are estimated by the DTW, which are further used to compensate the IMF segments. Finally, the compensated IMF segments are averaged to obtain the feature signal. Simulation and experimental results validate the efficiency of the algorithm in extracting feature signal from collected speed fluctuating signal without tachometer.