Abstract
To improve the accuracy of the prediction in avionics prognostics and health management (PHM), a variety of theories and methods are studied. In this paper, a prediction algorithm based on multiwavelet support vector machine(WSVM)is proposed. Multiwavelet denoising is used for signal data preprocessing. Then multiwavelet is employed to decompose the data into several subsequences at different scales. These subsequences are predicted by different support vector machines respectively. Finally, the final predicted results reconstituted from the subsequences are obtained. To validate the model, experiment data from a set of certain avionics voltage data is used. Predicted results of the proposed algorithm are validated to be more accurate than that of traditional support vector machine prediction algorithm. The mean square error (MSE) is decreased to 0.1956.
This work was supported by Science and Technology on Avionics Integration Laboratory and Aeronautical Science Fundaiton of China under Grant 20105581016.
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Zhou, X., Xiang, Z., Liu, M., Xiang, J. (2013). A Multiwavelet Support Vector Machine Prediction Algorithm for Avionics PHM. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_35
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DOI: https://doi.org/10.1007/978-3-642-39479-9_35
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