Pca based health indicator for remaining useful life prediction of wind turbine gearbox

Abstract

Fault prognosis of wind turbine gearbox has received considerable attention as it predicts the remaining useful life which further allows the scheduling of maintenance strategies. However, the studies related towards the RUL prediction of wind turbine gearbox are limited, because of the complexity of gearbox, acute changes in the operating conditions and non-linear nature of the acquired vibration signals. In this study, a health indicator is constructed in order to predict the remaining useful life of the wind turbine gearbox. Run to fail experiments are performed on a laboratory scaled wind turbine gearbox of overall gear ratio 1:100. Vibration signals are acquired and decomposed through continuous wavelet transform to obtain the wavelet coefficients. Various statistical features are computed from the wavelet coefficients which return form high-dimensional input feature set. Principal component analysis is performed to reduce the dimensionality and principal components (PCs) are computed from the input feature set. PC1 is considered as the health indicator and subjected to further smoothening by linear rectification technique. Exponential degradation model is fit to the considered health indicator and the model is able to predict the RUL of the gearbox with an error percentage of 2.73 %.