Research and Simulation of fault prediction method in numerical control machine tool operation
- 10.2991/icmmcce-15.2015.217How to use a DOI?
- numerical control machine tool; fault diagnosis; firefly algorithm
in the studying process of fault prediction method during operating numerical control (NC) machine tool, using the current algorithm to predict fault in the running process of the NC machine tool, iteration is easy to fall into the local extremum problem. Therefore, a new method of fault prediction based on the improved firefly algorithm for NC machine tool is proposed. First, the firefly algorithm is used to analyze the running process of a NC machine tool, and NC machine tool fault diagnosis model is established. Then, the background value optimization and NC machine tool fault diagnosis model are combined and applied in NC machine tools fault prediction, in the predicting process, the effect of background value and initial conditions on the fault prediction accuracy of the operation process of the NC machine tool, using iterative process to update the original data, and the particle swarm optimization algorithm is fused to optimize the background value of each iteration, in the end, NC machine tool fault prediction can be completed effectively. The simulation results prove that the method of fault prediction of the NC machine tool based on the improved firefly algorithm has high accuracy and robustness.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jinxia Chen PY - 2015/12 DA - 2015/12 TI - Research and Simulation of fault prediction method in numerical control machine tool operation BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SP - 871 EP - 874 SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.217 DO - 10.2991/icmmcce-15.2015.217 ID - Chen2015/12 ER -