Degradation prediction based on correlation analysis and assembled neural network

Efficient prognosis for remaining useful life of product is critical for both accelerated degradation testing and prognostics and health management, which are two hot points in recent years. A novel degradation prediction model was proposed to improve the long prediction capability for complex degradation path. Durbin-Watson method and partial correlation graph were utilized to analyze the decomposition results of wavelet transformation. Then, according to the characters of series, back propagation(BP) and wavelet neural network were assembled to predict degradation path. To verify the proposed method, wavelet neural network was selected as comparison. A practical degradation result demonstrates that this model can offer smaller mean square error (MSE) and higher prediction accuracy for remaining useful life (RUL).