Gear tooth fault detection by autoregressive modelling

Gears are important element in a variety of industrial applications. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. This paper present a gear tooth fault diagnosis technique of Autoregressive (AR) modeling of vibration signals. AR model coefficient is been determined by Yule-Walker equation with Levision-Durbin recursive algorithm. The model order is an essential part and is calculated by Akaike Information Criteria. The vibration signal of normal and faulty gear is been modeled and frequency response of AR model of the faulty gear is been compared with the AR model of the normal gear. The changes in the frequency spectrum indicate the fault.