Big Data Technology for Resilient Failure Management in Production Systems

被引:5
作者
Stich, Volker [1 ]
Jordan, Felix [1 ]
Birkmeier, Martin [1 ]
Oflazgil, Kerem [1 ]
Reschke, Jan [1 ]
Diews, Anna [1 ]
机构
[1] RWTH Aachen Univ FIR, Inst Ind Management, Aachen, Germany
来源
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: INNOVATIVE PRODUCTION MANAGEMENT TOWARDS SUSTAINABLE GROWTH (AMPS 2015), PT I | 2015年 / 459卷
关键词
Failure management; Big data; Complex event processing; Production control; Pattern management;
D O I
10.1007/978-3-319-22756-6_55
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to a growing complexity within value chains the susceptibility to failures in production processes increases. The research project BigPro explores the applicability of Big Data to realize a pro-active failure management in production systems. The BigPro-platform complements structured production data and unstructured human data to improve failure management. In a novel approach, the aggregated data is analyzed for reoccurring patterns that indicate possible failures of the production system, known from historic failure events. These patterns are linked to failures and respective countermeasures and documented in a catalog. The project results are validated in three industrial use cases.
引用
收藏
页码:447 / 454
页数:8
相关论文
共 12 条
[1]  
[Anonymous], 2012, ANAL REAL WORLD USE
[2]  
[Anonymous], 2006, AAAI SPRING S COMP A
[3]  
[Anonymous], 2001, META Group Research Note
[4]  
Bloehdorn S., 2014, BIG DATA TECHNOLOGIE
[5]  
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
[6]   Processing Flows of Information: From Data Stream to Complex Event Processing [J].
Cugola, Gianpaolo ;
Margara, Alessandro .
ACM COMPUTING SURVEYS, 2012, 44 (03)
[7]  
Goebbels S., 2004, GESCHAFTSPROZESS FME
[8]  
Grauer M, 2010, MULT WIRTSCH
[9]  
GRoTSCHEL M., 2001, ONLINE OPTIMIZATION
[10]  
McKinsey Digital, 2015, MCKINSEY DIG IND 4 0