A New Acoustic Emission Sensor Based Gear Fault Detection Approach

Yongzhi Qu, David He, Eric Bechhoefer, and Junda Zhu
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Publication Issue: 
Special Issue Wind Turbine PHM
Submission Type: 
Full Paper
ijphm_13_011.pdf943.18 KBJuly 18, 2013 - 10:28pm

In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to ensure the reliability and availability of wind turbines. A gearbox is an important component of a wind turbine. Therefore, developing effective gearbox fault detection tools is important to the PHM of wind turbine. 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 spectrum kurtosis (SK) to process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz 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.

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Publication Volume: 
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Submission Keywords: 
time synchronous average
gear fault detection
acoustic emission sensors
spectrum kurtosis
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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