Vibration Analysis and Time Series Prediction for Wind Turbine Gearbox Prognostics

Sajid Hussain and Hossam A.Gabbar
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Wind Turbine PHM
Submission Type: 
Full Paper
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ijphm_13_014.pdf805.91 KBMay 20, 2013 - 3:05pm

Premature failure of a gearbox in a wind turbine poses a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM) techniques are widely used to access the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals emanating from the gearbox. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS) and nonlinear autoregressive model with exogenous inputs (NARX). The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
014
Page Count: 
11
Submission Keywords: 
Vibration Analysis; Time Series Prediction; Wind Turbine Gearbox Prognostics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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