Data-Driven State Detection for an asset working at heterogenous regimens⁎⁎This work is supported by Lombardy funded project SMART4CPPS (ID: 236789 CUP: E19I18000000009). Nucera, D. D., Quadrini, W., Fumagalli, L., & Scipioni, M. P. IFAC-PapersOnLine, 54(1):1248–1253, January, 2021. ![Data-Driven State Detection for an asset working at heterogenous regimens⁎⁎This work is supported by Lombardy funded project SMART4CPPS (ID: 236789 CUP: E19I18000000009) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The current trend of industrial digitalization paved the way to Machine Learning applications which are adding value to data coming from the assets. In this context, the case study of a State Detection in an asset characterized by heterogeneous working regimens is proposed, with the aim of automatically recognizing the type of the ongoing production and of identifying its different operating conditions. The activity is executed by exploiting the data available on the asset controller and applying and comparing two different clustering algorithms, namely K-Means and HDBSCAN. The paper describes hence the application case and the adopted approaches, while providing insights on the most preferable choice for any of the two objectives, in order to pave the ground for condition-based maintenance activities.
@article{nucera_data-driven_2021,
series = {17th {IFAC} {Symposium} on {Information} {Control} {Problems} in {Manufacturing} {INCOM} 2021},
title = {Data-{Driven} {State} {Detection} for an asset working at heterogenous regimens⁎⁎{This} work is supported by {Lombardy} funded project {SMART4CPPS} ({ID}: 236789 {CUP}: {E19I18000000009})},
volume = {54},
issn = {2405-8963},
shorttitle = {Data-{Driven} {State} {Detection} for an asset working at heterogenous regimens⁎⁎{This} work is supported by {Lombardy} funded project {SMART4CPPS} ({ID}},
url = {https://www.sciencedirect.com/science/article/pii/S2405896321009137},
doi = {10.1016/j.ifacol.2021.08.149},
abstract = {The current trend of industrial digitalization paved the way to Machine Learning applications which are adding value to data coming from the assets. In this context, the case study of a State Detection in an asset characterized by heterogeneous working regimens is proposed, with the aim of automatically recognizing the type of the ongoing production and of identifying its different operating conditions. The activity is executed by exploiting the data available on the asset controller and applying and comparing two different clustering algorithms, namely K-Means and HDBSCAN. The paper describes hence the application case and the adopted approaches, while providing insights on the most preferable choice for any of the two objectives, in order to pave the ground for condition-based maintenance activities.},
language = {en},
number = {1},
urldate = {2021-11-15},
journal = {IFAC-PapersOnLine},
author = {Nucera, Domenico Daniele and Quadrini, Walter and Fumagalli, Luca and Scipioni, Marcello Paolo},
month = jan,
year = {2021},
keywords = {Clustering, HDBSCAN, K-Means, Production activity control, Quality assurance, State Detection, maintenance},
pages = {1248--1253},
}
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