Cycle Detection and Clustering for Cyber Physical Systems
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 655 LNNS, Page: 100-114
2023
- 3Citations
- 3Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In this paper we present our work on cycle detection and clustering using unsupervised Machine Learning methods on manufacturing data. First we discuss the overall architecture of our cyber-physical system specially designed to gather large quantities of heterogeneous industrial data. Next, we detail several analysis steps, focusing on core tasks such as cycle detection and identification. Finally we show that even relatively simple data can be successfully used for predictive maintenance and fault detection.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85151046967&origin=inward; http://dx.doi.org/10.1007/978-3-031-28694-0_10; https://link.springer.com/10.1007/978-3-031-28694-0_10; https://dx.doi.org/10.1007/978-3-031-28694-0_10; https://link.springer.com/chapter/10.1007/978-3-031-28694-0_10
Springer Science and Business Media LLC
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