Process Mining of Mining Processes: Analyzing Longwall Coal Excavation Using Event Data

被引:1
|
作者
Brzychczy, Edyta [1 ]
Zuber, Agnieszka [2 ]
Aalst, Wil van der [3 ]
机构
[1] AGH Univ Krakow, Fac Mech Engn & Robot, PL-30059 Krakow, Poland
[2] AGH Univ Sci & Technol, Fac Civil Engn & Resource Management, PL-30059 Krakow, Poland
[3] Rhein Westfal TH Aachen, Proc & Data Sci Grp, D-52062 Aachen, Germany
关键词
Data mining; Task analysis; Analytical models; Data models; Robot sensing systems; Process modeling; Monitoring; Coal mining; industrial processes; longwall mining; PM; sensor data;
D O I
10.1109/TSMC.2023.3348496
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The mining industry faces many challenges, prompting the adoption of new technologies and continuous improvement of processes to improve operational efficiency and personnel safety. Using data from information systems combined with novel process mining (PM) techniques creates new possibilities for improving industrial processes. This article presents a comprehensive method of modeling and analyzing the longwall process in underground mining based on event data using process mining (PM4LMP). The method comprises four basic steps: 1) data gathering; 2) data preprocessing; 3) creation of event logs; and 4) PM tasks. In our method, we proposed, among all, case ID identification based on heuristics using context data and activity identification with supervised and unsupervised approaches, which provide complementary information about process execution. The method assumes an in-depth analysis of processes based on sensor data and knowledge gathered in IT systems, which can significantly improve the quality of information at managers' disposal when making decisions regarding the mining process.
引用
收藏
页码:3231 / 3243
页数:12
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