Signal-based Context Comparative Analysis for Identification of Similar Manufacturing Modules
Publication date
2018-01-01
Document type
Research article
Author
Organisational unit
Scopus ID
Conference
16th IFAC Symposium on Information Control Problems in Manufacturing (INCOM), 11.-13.06.2018, Bergamo, Italy
Series or journal
IFAC-PapersOnLine
Periodical volume
51
Periodical issue
11
First page
276
Last page
283
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
Behavior Model
Pattern Matching
Petri Net
Sequence Alignment
Abstract
Industry 4.0 connects different machines and their modules to each other. Integrating already existing non-modular machines and establishing the required modularization in such a scenario requires a lot of time-consuming analysis. But Industry 4.0 also allows previously unconnected machines to establish a comparative analysis between each other by comparing monitored results of new and old machines. This analysis allows finding behavior that overlaps between machines and allows to identify parts that can be encapsulated in new or substituted by already known modules. In this paper, we propose a method to identify those overlapping parts by exploiting learned behavior models during runtime and combining production paths with sequence search algorithms by using context information of observable event signals.
Version
Not applicable (or unknown)
Access right on openHSU
Metadata only access