A Multiple Model Prediction Algorithm for CNC MachineWear PHM
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Published
Jun 1, 2011
Huimin Chen
Abstract
We present a multiple model approach for wear depth estimation of milling machine cutters using dynamometer, accelerometer, and acoustic emission data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The performance evaluation procedure and the resulting scores from the submitted predictions are also discussed.
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Keywords
multiple model fusion, PHM data challenge
References
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Chen, H., Bart, H., & Huang, S. (2008). Integrated Feature Selection and Clustering for Taxonomic Problems within Fish Species Complexes. Journal of Multimedia, 3(3), 10-17.
Chen, H., & Huang, S. (2005). A Comparative Study on Model Selection and Multiple Model Fusion. In International Conference on Information Fusion.
Li, X., Lim, B., Zhou, J., Huang, S., Phua, S., Shaw, K., et al. (2009). Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation. In Annual Conference of the Prognostics and Health Management Society.
MacKay, D. (1992). Bayesian Interpolation. Neural Computation, 4, 415-447.
Rissanen, J. (1996). Fisher Information and Stochastic Complexity. IEEE Trans. on Information Theory, 42, 40-47.
Schervish, M. (1996). P Values: What They Are and What They Are Not. The American Statistician, 50(3), 203-206.
Chen, H., Bart, H., & Huang, S. (2008). Integrated Feature Selection and Clustering for Taxonomic Problems within Fish Species Complexes. Journal of Multimedia, 3(3), 10-17.
Chen, H., & Huang, S. (2005). A Comparative Study on Model Selection and Multiple Model Fusion. In International Conference on Information Fusion.
Li, X., Lim, B., Zhou, J., Huang, S., Phua, S., Shaw, K., et al. (2009). Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation. In Annual Conference of the Prognostics and Health Management Society.
MacKay, D. (1992). Bayesian Interpolation. Neural Computation, 4, 415-447.
Rissanen, J. (1996). Fisher Information and Stochastic Complexity. IEEE Trans. on Information Theory, 42, 40-47.
Schervish, M. (1996). P Values: What They Are and What They Are Not. The American Statistician, 50(3), 203-206.
Section
Technical Briefs