Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
711849 | IFAC Proceedings Volumes | 2007 | 6 Pages |
Abstract
AbstractA monitoring system for the cutting tool condition for an industrial machining center is proposed. Four matching patterns and stochastic modelling approaches (Artificial Neural Network, Learning Vector Quantization, Support Vector Machine, and Hidden Markov Model) are compared for the diagnosis step. Integration of several sensor signals into a single fused estimation is considered. Several performance indexes such as binary and multiple classification, false alarm and false fault rate, and operating costs are considered for the comparison. Early results show that Hidden Markov Model-based approach fusing three sensors outperforms other techniques and exhibits 98% efficiency.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Rubén Morales-Menéndez, Antonio Vallejo Jr, Juan A. Nolazco-Flores, Paola García-Perera,