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.