Szczegóły publikacji
Opis bibliograficzny
Process-oriented approach for analysis of sensor data from longwall monitoring system / Edyta BRZYCHCZY, Agnieszka TRZCIONKOWSKA // W: Intelligent Systems in Production Engineering and Maintenance : [ISPEM 2018 : Wrocław, 17-18.09.2018] / eds. Anna Burduk [et al.]. — Cham : Springer Nature Switzerland AG, cop. 2019. — (Advances in Intelligent Systems and Computing ; ISSN 2194-5357 ; vol. 835). — ISBN: 978-3-319-97489-7; e-ISBN: 978-3-319-97490-3. — S. 611–621. — Bibliogr. s. 619–621, Abstr. — Publikacja dostępna online od: 2018-08-01
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 115937 |
|---|---|
| Data dodania do BaDAP | 2018-09-11 |
| DOI | 10.1007/978-3-319-97490-3_58 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Second international conference on Intelligent Systems in Production Engineering and Maintenance |
| Czasopismo/seria | Advances in Intelligent Systems and Computing |
Abstract
In the paper we address possibility of industrial process analysis based on sensor data with the use of process-oriented analytic techniques. We propose in this area usage of process mining techniques. Important issue in the context of usefulness of industrial sensor data gathered in the monitoring systems for process analysis is proper level of abstraction of an event log. We present our approach requiring creation of high-level event logs based on low-level events from longwall monitoring system in order to model and analyse the mining process in an underground mine. We use combination of unsupervised data mining techniques as well as domain knowledge to discover stages in an example process and create an event logs for further process analysis.