Prediction algorithms using specialized software tools for steel industry equipment

被引：0
|
作者：
Raducan, E. [1 ]
Nicolau, V [2 ]
Andrei, M. [2 ]
Petrea, G. [2 ]
Vlej, G. M. [3 ]
机构：
[1] Dunarea de Jos Univ Galati, Dept Automat & Elect Engn, Galati, Romania
[2] Dunarea de Jos Univ Galati, Dept Elect & Telecommun, Galati, Romania
[3] Liberty Steel Grp, Dept Automat Digitalizat, Galati, Romania
来源：
2020 IEEE 26TH INTERNATIONAL SYMPOSIUM FOR DESIGN AND TECHNOLOGY IN ELECTRONIC PACKAGING (SIITME 2020) | 2020年
关键词：
Turbo blowers; maintenance; predictive models; machine learning; steel industry;
D O I：
10.1109/siitme50350.2020.9292146
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The paper aims to present a model for predictive maintenance applicable in steel industry for critic equipment. This paper investigates the application of multi-step time prediction to sustain the turbo blowers (TB) equipment prognostics using software analytics algorithms which describe forecasting models, statistical approach or the formulas specifies for industrial equipment developed based by DAX formulas. This application represent a new method for realize a predictive maintenance describe as an industrial revolution characterized by smart systems and Internet-based solutions.
引用
收藏
页码：174 / 177
页数：4
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