When compared 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 manufacturing community 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 has been conducted.
In this paper, we investigated fault diagnosis for STG using both vibration and acoustic emission (AE) signals. In particular, seeded fault tests on a STG type gearbox were conducted to collect both vibration and AE signals. Gear fault features were extracted from vibration signals using a Hilbert-Huang Transform (HHT) based algorithm and from AE signals using AE analysis, respectively. These fault features were used for fault detection using a K-nearest neighbor (KNN) algorithm. Our investigation has shown that both vibration and AE signals were capable of detecting the gear fault in a STG. However, in terms of locating the source of the fault, AE analysis outperformed vibration analysis.
How to Cite
diagnosis, fault diagnosis, features, gears, applications: helicopter
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