{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:18:34Z","timestamp":1783145914504,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Monitoring industrial infrastructures are undergoing a critical transformation with industry 4.0.\u00a0 Monitoring solutions must follow the\u00a0system behavior in real time and must adapt to its continuous change. We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application. A set of sensors collects data from turbo-compressors and an original two-level machine learning LSTM autoencoder architecture defines a continuous nominal vibration model. Normalized thresholds (ISO 20816) between the model and the system generates a possible abnormal situation to diagnose. Experimental results, including hyper-parameter optimization on large real data and domain expert analysis, show that our proposed solution gives promising results.\u00a0<\/jats:p>","DOI":"10.24963\/ijcai.2019\/254","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"1836-1843","source":"Crossref","is-referenced-by-count":9,"title":["Monitoring of a Dynamic System Based on Autoencoders"],"prefix":"10.24963","author":[{"given":"Aomar","family":"Osmani","sequence":"first","affiliation":[{"name":"Laboratoire LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massinissa","family":"Hamidi","sequence":"additional","affiliation":[{"name":"Laboratoire LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salah","family":"Bouhouche","sequence":"additional","affiliation":[{"name":"Industrial Technologies Research Center, CRTI-DTSI, Algiers, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:47:54Z","timestamp":1564300074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/254"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/254","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}