{"id":93855,"date":"2020-04-15T12:00:00","date_gmt":"2020-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/process-stability-prediction-with-machine-learning\/"},"modified":"2024-09-25T14:24:46","modified_gmt":"2024-09-25T12:24:46","slug":"process-stability-prediction-with-machine-learning","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/process-stability-prediction-with-machine-learning\/","title":{"rendered":"Process Stability Prediction with Machine Learning"},"content":{"rendered":"<p><!-- GITO PUB shortened content start --><\/p>\n<div class=\"gito-pub-frontend-post-protected\"><\/div>\n<p><!-- GITO PUB shortened content end --><\/p>\n<div style=\"display:block;text-align:center !important;margin:30px 0;\">\n<div style=\"display:inline-block;border:1px solid #555;padding:10px;max-width:600px;text-align:left;\">\n<h2 class=\"gito-pub-download-section\"><center>Access limited<\/center><\/h2>\n<p>You are currently <strong>not logged in \/ not yet registered<\/strong>.<\/p>\n<p>In order to download the desired file(s), you must be logged in and have an appropriate inclusive subscription. 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In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.<\/p>\n","protected":false},"featured_media":96201,"menu_order":0,"template":"","categories":[],"tags":[80028,79359,80025,80079,80152],"product_cat":[3300],"topic":[],"technology":[67790],"knowhow":[],"industry":[],"writer":[83140,83138,83137,83139,80830],"content-type":[],"potential":[],"solution":[67678],"glossary":[],"class_list":{"0":"post-93855","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"tag-artificial-intelligence-en-2","7":"tag-deep-learning-en","8":"tag-kuenstliche-intelligenz-en","9":"tag-predictive-maintenance-en","10":"tag-recurrent-neural-networks-en","11":"product_cat-article","12":"technology-artificial-intelligence","13":"writer-bjoern-krause-en","14":"writer-hubert-wuerschinger-en","15":"writer-matthias-muehlbauer-en","16":"writer-moritz-schmehling-en","17":"writer-nico-hanenkamp-en","18":"solution-maintenance","19":"product","20":"first","21":"instock","22":"downloadable","23":"virtual","24":"sold-individually","25":"taxable","26":"purchasable","27":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/muehlbauer_Vorhersage-der-Prozessstabilitaet-mit-MaschinellemLernen_IM2020_2-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-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Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/93855","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/96201"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=93855"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=93855"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=93855"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=93855"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=93855"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=93855"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=93855"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=93855"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=93855"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=93855"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=93855"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=93855"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=93855"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}