Research on industrial robot reducer state monitoring cloud platform
Tielin Lu, Zitian Fan, Xiaowen Li, Rui Cao, Shuo Wang, Xiaojing Liu, Lu Ding, Wei Wei
- Year
- 2020
- Citations
- 3
Abstract
In this paper we proposed a method of monitoring industrial robot reducer online cloud platform. Based on the theory of cyber physical model, a cloud state monitoring system is designed due to the problems of complex objects, massive failure information, high stable demand and poor real-time control ability in the modern industrial robot maintain. It includes intelligent sensor and edge computing to network cloud technology. With data model, the system uses deep learning and over-limit learning algorithm on the cloud management center server. The deep learning method trains and predicts the collected fault data, and can predict the malfunction of vector reducer. Further, the intelligent optimization scheduling algorithm is used in the cloud to obtain real-time fault in industrial robot reducer. The problem of distribution and the dynamic running performance of cloud system is monitored and improved.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002