Improvement and Application of Grey Wave Prediction Model Based on PHM of Civil Aircraft System

Aiming at evaluating parameters with small sample numbers considering unobvious development trends and irregular fluctuations in civil aircraft system prognosties and health management, this paper proposes a grey wave prediction optimization model based on the improved grey effect amount and whitening equation. A K-value clustering method is first applied to determine main data contours to determine the intersection of the main contours and the original data waves. The grey effect amount and whitening equations in GM(1,1) prediction model are then optimized, as well as modeling and fitting the contour time sequence. The verification is performed on the A320 air conditioning system, and the model performance is analyzed. The verification and comparison analysis shows that the our improved model has a prediction accuracy of 9.33% with irregular waves, which outperforms the conventional model with accuracy of 77.8%. The proposed model presents a good fitting and can predict irregular waves which is a characteristic in the health management showing significant impacts on the civil aircraft system.