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We present a semi-structured approach that uses observations from the manual classification of time series and combines different algorithms to partition the set of signals into smaller groups of signals that share common characteristics. Using a real-world dataset from several power plants, we evaluate our solution for scaling-invariant measurement identification and functional relationship inference using change-point correlations.<\/jats:p>","DOI":"10.1515\/itit-2023-0051","type":"journal-article","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T08:40:38Z","timestamp":1696668038000},"page":"177-188","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning in sensor identification for industrial systems"],"prefix":"10.1515","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-6935","authenticated-orcid":false,"given":"Lucas","family":"Weber","sequence":"first","affiliation":[{"name":"Technical Faculty \u2013 Department of Computer Science Chair 6 , Friedrich-Alexander Universit\u00e4t Erlangen , D-91058 Erlangen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1551-4824","authenticated-orcid":false,"given":"Richard","family":"Lenz","sequence":"additional","affiliation":[{"name":"Technical Faculty \u2013 Department of Computer Science Chair 6 , Friedrich-Alexander Universit\u00e4t Erlangen , D-91058 Erlangen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"2023112113471174075_j_itit-2023-0051_ref_001","doi-asserted-by":"crossref","unstructured":"P. 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