Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing pro...
Main Authors: | Vadim Kapp, Marvin Carl May, Gisela Lanza, Thorsten Wuest |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Journal of Manufacturing and Materials Processing |
Subjects: | |
Online Access: | https://www.mdpi.com/2504-4494/4/3/88 |
Similar Items
-
Digital Twins From Smart Manufacturing to Smart Cities: A Survey
by: Georgios Mylonas, et al.
Published: (2021-01-01) -
Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects
by: Paul Grefen, et al.
Published: (2022-01-01) -
Six-Gear Roadmap towards the Smart Factory
by: Amr T. Sufian, et al.
Published: (2021-04-01) -
Lean Manufacturing in Industry 4.0: A Smart and Sustainable Manufacturing System
by: Benedictus Rahardjo, et al.
Published: (2023-01-01) -
Smart Hybrid Manufacturing Control Using Cloud Computing and the Internet-of-Things
by: Jonnro Erasmus, et al.
Published: (2018-12-01)