ACCURACY IMPROVEMENT IN WEAR STATE DISCONTINUOUS TRACKING MODEL REGARDING STATISTICAL DATA INACCURACIES AND SHIFTS WITH BOOSTING MINI-ENSEMBLE OF TWO-LAYER PERCEPTRONS
Abstract
There is presented a method of improving accuracy in tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is pre-sumed to be wholly sampled into those factors. The tracker is a static model based on boosting mini-ensemble of three two-layer perceptrons with nonlinear transfer functions. It regards statistical data inaccuracies and shifts. For making the ensem-ble, the AdaBoost technique is used. A distinction of the presented method of boosting from the AdaBoost is in the rule for finding the decreasing coefficient in order to re-distribute weights over training samples. Another one is that the ensemble is aggregated at once. The averaged gain of the boosting mini-ensemble in tracking 24 wear states with 16 influencing factors exceeds 50 %. The wear state tracking model is going to be perfected on optimizing two parameters of the training set and the naive rule for finding the decreasing coefficient before re-distributing training samples’ weights.References
1. Chungchoo C. On-line tool wear estimation in CNC turning operations using fuzzy neural network model / C. Chungchoo, D. Saini // International Journal of Machine Tools and Manufacture. – 2002. – Volume 42, Issue 1. – P. 29-40.
2. Romanuke V. V. Wear state discontinuous tracking model as two-layer perceptron with nonlinear transfer functions being trained on an extended general totality regarding statistical data inaccuracies and shifts / V. V. Romanuke // Problems of tribology. – 2014. – N. 3. – P. 50- 53.
3. Champion M. Sparse regression and support recovery with image-Boosting algorithms / M. Cham-pion, C. Cierco-Ayrolles, S. Gadat, M. Vignes // Journal of Statistical Planning and Inference. – 2014. – Volume 155. – P. 19-41.
4. Shen C. Fully corrective boosting with arbitrary loss and regularization / C. Shen, H. Li, A. van den Hengel // Neural Networks. – 2013. – Volume 48. – P. 44-58.
5. Hua J. A cobalt diffusion based model for predicting crater wear of carbide tools in machining tita-nium alloys / J. Hua, R. Shivpuri // Journal of Engineering Materials and Technology. – 2005. – Volume 127. – P. 136-144.
6. Nie Q. Probability estimation for multi-class classification using AdaBoost / Q. Nie, L. Jin, S. Fei // Pattern Recognition. – 2014. – Volume 47, Issue 12. – P. 3931-3940.
7. Sun Y. Unifying multi-class AdaBoost algorithms with binary base learners under the margin frame-work / Y. Sun, S. Todorovic, J. Li // Pattern Recognition Letters. – 2007. – Volume 28, Issue 5. – P. 631-643.
2. Romanuke V. V. Wear state discontinuous tracking model as two-layer perceptron with nonlinear transfer functions being trained on an extended general totality regarding statistical data inaccuracies and shifts / V. V. Romanuke // Problems of tribology. – 2014. – N. 3. – P. 50- 53.
3. Champion M. Sparse regression and support recovery with image-Boosting algorithms / M. Cham-pion, C. Cierco-Ayrolles, S. Gadat, M. Vignes // Journal of Statistical Planning and Inference. – 2014. – Volume 155. – P. 19-41.
4. Shen C. Fully corrective boosting with arbitrary loss and regularization / C. Shen, H. Li, A. van den Hengel // Neural Networks. – 2013. – Volume 48. – P. 44-58.
5. Hua J. A cobalt diffusion based model for predicting crater wear of carbide tools in machining tita-nium alloys / J. Hua, R. Shivpuri // Journal of Engineering Materials and Technology. – 2005. – Volume 127. – P. 136-144.
6. Nie Q. Probability estimation for multi-class classification using AdaBoost / Q. Nie, L. Jin, S. Fei // Pattern Recognition. – 2014. – Volume 47, Issue 12. – P. 3931-3940.
7. Sun Y. Unifying multi-class AdaBoost algorithms with binary base learners under the margin frame-work / Y. Sun, S. Todorovic, J. Li // Pattern Recognition Letters. – 2007. – Volume 28, Issue 5. – P. 631-643.
Downloads
Published
2015-06-18
How to Cite
Romanuke, V. (2015). ACCURACY IMPROVEMENT IN WEAR STATE DISCONTINUOUS TRACKING MODEL REGARDING STATISTICAL DATA INACCURACIES AND SHIFTS WITH BOOSTING MINI-ENSEMBLE OF TWO-LAYER PERCEPTRONS. Problems of Tribology, 74(4), 55–58. Retrieved from https://tribology.khnu.km.ua/index.php/ProbTrib/article/view/408
Issue
Section
Articles
