Research on Airplane Health Forecast Method Based on the Improved MGM(1, n)
There is strong randomness and uncertainty lying in the health status of airplane. Because the former grey models can not forecast the random signals efficiently, a new forecast method for the health state of airplane, based on the improved MGM(1, n), is presented in this paper. The advanced acoustic emission (AE) technique is used to collect the health state information. The original AE signals are decomposed with the wavelet transform. The energy values, eigenvalues and standard deviation of the third layer wavelet decomposition low frequency coefficients are respectively extracted to form eigenvectors. Then the improved MGM(1, n) is established by these eigenvectors. The improved algorithm is realized by feeding back the errors between the forecast values and the actual ones so as to improve the forecast precision. Experiments show that the improved MGM(1, n) can forecast the airplane stabilizer fatigue crack more accurately than the GM (1, 1). And this new method has been successfully applied to the forecast system of the health state of airplane structure components.
Airplane Health Forecast Acoustic Emission MGM(1 n)
Jianguo Cui Desheng Song Shiliang Dong Mingzhuo Wang Xiaopeng Liang Xinhe Xu
Automatization College, Shenyang Institute of Aeronautical Engineering, Shenyang 110136, China;Schoo Automatization College, Shenyang Institute of Aeronautical Engineering, Shenyang 110136, China; Shenyang Aircraft Design & Research Institute, Shenyang 110035, China School of Information Science & Engineering, Northeastern University, Shenyang 110004, China;
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5262-5265
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）