Name
Abstract | ||
|---|---|---|
The analysis of manufacturing processes through process mining requires meaningful log data. Regarding worker activities, this data is either sparse or costly to gather. The primary objective of this paper is the implementation and evaluation of a system that detects, monitors and logs such worker activities and generates meaningful event logs. The system is light-weight regarding its setup and convenient for instrumenting assembly workstations in job shop manufacturing for temporary observations. In a study, twelve participants assembled two different product variants in a laboratory setting. The sensor events were compared to video annotations. The optical detection of grasping material by RGB cameras delivered a Median F-score of 0.83. The RGB+D depth camera delivered only a Median F-score of 0.56 due to occlusion. The implemented activity detection proofs the concept of process elicitation and prepares process mining. In future studies we will optimize the sensor setting and focus on anomaly detection. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-74030-0_20 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Process elicitation,Activity recognition,Manufacturing | Anomaly detection,Activity recognition,Systems engineering,Computer science,Job shop,Workstation,Mathematical proof,Activity detection,RGB color model,Artificial intelligence,Machine learning,Process mining | Conference |
Volume | ISSN | Citations |
308 | 1865-1348 | 1 |
PageRank | References | Authors |
0.37 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sönke Knoch | 1 | 1 | 0.37 |
| Shreeraman Ponpathirkoottam | 2 | 4 | 0.80 |
| Peter Fettke | 3 | 812 | 78.37 |
| Peter Loos | 4 | 479 | 40.84 |