{"id":2126,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=2126"},"modified":"2021-07-13T18:58:22","modified_gmt":"2021-07-13T16:58:22","slug":"enhancing-cooling-tower-performance-with-condition-monitoring-and-machine-learning-based-drift-detection","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/machine-tools-anomaly-detection\/enhancing-cooling-tower-performance-with-condition-monitoring-and-machine-learning-based-drift-detection\/","title":{"rendered":"Enhancing cooling tower performance with condition monitoring and machine learning based drift detection"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by <em>Sina Nahvi, Stefan Polster, Sebastian Melzer, Anke Stoll, Marc Muennich, Stefan Mannstadt, Philipp Kliman<\/em><\/em> <em>(Germany)<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Process cooling is crucial to many manufacturing processes. To monitor the performance of a cooling tower, it was equipped with extensive sensors for internal and environmental data acquisition. The aim is to improve reactive and predictive maintenance by estimating the actual condition as well as predicting defective behavior of the cooling tower. We designed a method, which derives the degree of defect from data of the non-defective cooling tower. A concept drift detection approach was implemented, which monitors the model estimation error of a multilayer perceptron model. Increasing model estimation error indicates changing system behavior and increasing risk of failure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Condition monitoring, Fault detection, Neural Network, Concept drift, Cooling Tower<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Video presentation<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"http:\/\/cirpicme.org\/wp-content\/uploads\/2021\/06\/Sina_Nahvi.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presenting author<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-subtle-light-gray-background-color has-background\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"180\" class=\"wp-image-2274\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Sina_Nahvi.jpeg?resize=150%2C180\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Sina_Nahvi.jpeg?w=354&amp;ssl=1 354w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Sina_Nahvi.jpeg?resize=250%2C300&amp;ssl=1 250w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Sina_Nahvi.jpeg?resize=125%2C150&amp;ssl=1 125w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Sina Nahvi<br><br>Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany<br><br>sina.nahvi@iwu.fraunhofer.de<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Sina Nahvi, Stefan Polster, Sebastian Melzer, Anke Stoll, Marc Muennich, Stefan Mannstadt, Philipp Kliman (Germany) Abstract Process cooling is crucial to many manufacturing processes. To monitor the performance of a cooling tower, it was equipped with extensive sensors for internal and environmental data acquisition. The aim is to improve&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/machine-tools-anomaly-detection\/enhancing-cooling-tower-performance-with-condition-monitoring-and-machine-learning-based-drift-detection\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":2311,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-2126","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2126","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=2126"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2126\/revisions"}],"predecessor-version":[{"id":2300,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2126\/revisions\/2300"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2311"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=2126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}