Abstract
In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) toprocess AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.
Highlights
The largest variable cost to owners and operators of wind turbines is unscheduled maintenance
The acoustic emission (AE) data after heterodyning were collected by a low sampling rate device, with the sampling rate fixed at 100 kHz
In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce the operations and maintenance costs associated with running a wind farm
Summary
The largest variable cost to owners and operators of wind turbines is unscheduled maintenance. In wind turbines, this might be the difference between an up tower maintenance effect or a down tower event, which requires a crane (a large fixed expense). This might be the difference between an up tower maintenance effect or a down tower event, which requires a crane (a large fixed expense) It could be the difference between refurbishing a gearbox instead of replacing it. The development of effective gearbox fault detection tools is important to the PHM of wind turbine. AE, on the other hand, does not measure acceleration and is not a function of displacement: it is independent of shaft rate This has been observed in (Al-Ghamd & Mba, 2006), where early fault signatures were not present in vibration data, but was detected by AE. The development of a new AE sensor based gear fault detection approach is presented
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