@article{hennig_comparison_2020,
	series = {53rd {CIRP} {Conference} on {Manufacturing} {Systems} 2020},
	title = {Comparison of {Time} {Series} {Clustering} {Algorithms} for {Machine} {State} {Detection}},
	volume = {93},
	issn = {2212-8271},
	url = {http://www.sciencedirect.com/science/article/pii/S2212827120307149},
	doi = {10.1016/j.procir.2020.03.084},
	abstract = {New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data.},
	language = {en},
	urldate = {2020-09-28},
	journal = {Procedia CIRP},
	author = {Hennig, Martin and Grafinger, Manfred and Gerhard, Detlef and Dumss, Stefan and Rosenberger, Patrick},
	month = jan,
	year = {2020},
	keywords = {Industry 4.0, Internet of Things, Machine Learning, Predictive Maintenance, Time Series Clustering, Unsupervised Learning},
	pages = {1352--1357},
}

{"_id":"4ExquSXPqKuXWiajB","bibbaseid":"hennig-grafinger-gerhard-dumss-rosenberger-comparisonoftimeseriesclusteringalgorithmsformachinestatedetection-2020","author_short":["Hennig, M.","Grafinger, M.","Gerhard, D.","Dumss, S.","Rosenberger, P."],"bibdata":{"bibtype":"article","type":"article","series":"53rd CIRP Conference on Manufacturing Systems 2020","title":"Comparison of Time Series Clustering Algorithms for Machine State Detection","volume":"93","issn":"2212-8271","url":"http://www.sciencedirect.com/science/article/pii/S2212827120307149","doi":"10.1016/j.procir.2020.03.084","abstract":"New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data.","language":"en","urldate":"2020-09-28","journal":"Procedia CIRP","author":[{"propositions":[],"lastnames":["Hennig"],"firstnames":["Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Grafinger"],"firstnames":["Manfred"],"suffixes":[]},{"propositions":[],"lastnames":["Gerhard"],"firstnames":["Detlef"],"suffixes":[]},{"propositions":[],"lastnames":["Dumss"],"firstnames":["Stefan"],"suffixes":[]},{"propositions":[],"lastnames":["Rosenberger"],"firstnames":["Patrick"],"suffixes":[]}],"month":"January","year":"2020","keywords":"Industry 4.0, Internet of Things, Machine Learning, Predictive Maintenance, Time Series Clustering, Unsupervised Learning","pages":"1352–1357","bibtex":"@article{hennig_comparison_2020,\n\tseries = {53rd {CIRP} {Conference} on {Manufacturing} {Systems} 2020},\n\ttitle = {Comparison of {Time} {Series} {Clustering} {Algorithms} for {Machine} {State} {Detection}},\n\tvolume = {93},\n\tissn = {2212-8271},\n\turl = {http://www.sciencedirect.com/science/article/pii/S2212827120307149},\n\tdoi = {10.1016/j.procir.2020.03.084},\n\tabstract = {New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data.},\n\tlanguage = {en},\n\turldate = {2020-09-28},\n\tjournal = {Procedia CIRP},\n\tauthor = {Hennig, Martin and Grafinger, Manfred and Gerhard, Detlef and Dumss, Stefan and Rosenberger, Patrick},\n\tmonth = jan,\n\tyear = {2020},\n\tkeywords = {Industry 4.0, Internet of Things, Machine Learning, Predictive Maintenance, Time Series Clustering, Unsupervised Learning},\n\tpages = {1352--1357},\n}\n\n","author_short":["Hennig, M.","Grafinger, M.","Gerhard, D.","Dumss, S.","Rosenberger, P."],"key":"hennig_comparison_2020","id":"hennig_comparison_2020","bibbaseid":"hennig-grafinger-gerhard-dumss-rosenberger-comparisonoftimeseriesclusteringalgorithmsformachinestatedetection-2020","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S2212827120307149"},"keyword":["Industry 4.0","Internet of Things","Machine Learning","Predictive Maintenance","Time Series Clustering","Unsupervised Learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["yanwtMpCcFaHzRwWb","iwKepCrWBps7ojhDx"],"keywords":["industry 4.0","internet of things","machine learning","predictive maintenance","time series clustering","unsupervised learning"],"search_terms":["comparison","time","series","clustering","algorithms","machine","state","detection","hennig","grafinger","gerhard","dumss","rosenberger"],"title":"Comparison of Time Series Clustering Algorithms for Machine State Detection","year":2020}