Intelligent Built-in Test Fault Diagnosis and Prediction for Mechatronics Equipment

This paper proposes an intelligent Built-in Test (BIT) technology based on wavelet packet analysis and gray neural network. The aim is to improve the fault diagnosis and prediction capability of intelligent BIT. Firstly, the energy of each frequency-band was computed to form the eigenvectors by using the wavelet packet decomposition, then the energy eigenvectors were used as samples to the forecasting model, which were based on wavelet packet analysis and gray neural network. Finally, the proposed method was applied to the BIT system of the airborne mechatronics, and the results have shown that the proposed method could improve the performance of the intelligent BIT system.