Split torque type gearbox fault detection using acoustic emission and vibration sensors

Abstract

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven the helicopter OEMs to develop products using the STG. However, this may pose a challenge for the current gear analysis methods used in Health and Usage Monitoring Systems (HUMS). Gear analysis uses time synchronous averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous components (gears or bearing) in close proximity may significantly reduce the fault signal (decreased signal to noise) and therefore reduce the effectiveness of current gear analysis algorithms. As of today, only a limited research on STG fault diagnosis using vibration sensors has been conducted. In this paper, an investigation on STG fault detection using both vibration and acoustic emission (AE) sensors is reported. In particular, signals of both vibration and AE sensors on a notational STG type gearbox were collected from seeded fault tests. Gear fault features were extracted from vibration signals using a Hilbert-Huang Transform (HHT) based algorithm and from AE signals using AE analysis. These fault features were input to a K-nearest neighbor (KNN) algorithm for fault detection. The investigation results showed that both vibration and AE sensors were capable of detecting the gear fault in a STG. However, in terms of locating the source of the fault, AE sensors outperformed vibration sensors.