Gear Fault Diagnosis via Non-Stationary Adaptive MARTIN Distance

In this paper, a new method of gear fault diagnosis is proposed based on a combination of the Time Synchronized Averaging method (TSA), time-varying ARMA model and MARTIN distance. This method contains three major steps. In the first step, a TSA method is proposed for averaging the gearbox signal. The second step deals with selection of a proper ARMA model for a signal produced via a gearbox and using an adaptive filter (with a weighted least square algorithm) for identifying the timevarying parameters of the model. In the last step, a new time-varying distance is defined for gear fault diagnosis. The proposed distance is an extension of the MARTIN distance. Finally, as the case study, the method is used on a YAMAHA gearbox for identifying gear faults. The results of the diagnosis are satisfactory.