{"id":94179,"date":"2021-04-15T12:00:00","date_gmt":"2021-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=94179"},"modified":"2024-09-25T14:15:45","modified_gmt":"2024-09-25T12:15:45","slug":"approach-to-the-condition-description-of-technical-components","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/approach-to-the-condition-description-of-technical-components\/","title":{"rendered":"Approach to the Condition Description of Technical Components"},"content":{"rendered":"<hr>\n<div class=\"gito-pub-content-bibliography\">\n<h2>Bibliography <\/h2>\n[1] Steinhilper, W.; Sauer, B.: Konstruktionselemente des Maschinenbaus 2, 7. Auflage. Berlin Heidelberg 2012.<br \/>\n[2] Zhang, S.: Instandhaltung und Anlagenkosten. Wiesbaden 1990.<br \/>\n[3] Schiefer, H.; Schiefer, F.: Statistik f\u00fcr Ingenieure. Wiesbaden 2018.<br \/>\n[4] Siebertz, K.; van Bebber, D.; Hochkirchen, T.: Statistische Versuchsplanung, 2. Auflage. Berlin Heidelberg 2017.<br \/>\n[5] Walther Flender GmbH: Exakt kalkulierbare Lebensdauer von Zahnriemenantrieben mit L.E.A.N Drive. URL: www.walther-flender.de\/de\/lean-drive-lebensdauer-berechnung, Abrufdatum 12.11.2020.<br \/>\n[6] Bender, A.: Entwicklung eines Condition Monitoring Systems f\u00fcr Gummi-Metall-Elemente. In: Verlagsschriftenreihe Des Heinz Nixdorf Instituts 369 (2017), S. 347-358.<br \/>\n[7] Susmita, R.: A Quick Review of Machine Learning Algorithms. In: International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (2019), S. 35-39.<\/div>\n<div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\">\n<h2>Your downloads<\/h2>\n<p><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"94179\" data-userid =\"0\" data-filename=\"egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF<\/button><\/div>\n<br>Solutions: <span class=\"gito-pub-tag-element\"><a href=\"\/en\/functions\/maintenance\/\">Maintenance<\/a><\/span> <div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/load-cycles-en\/\">load cycles<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/machine-learning-en\/\">machine learning<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/prediction-model-en\/\">prediction model<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/predictive-maintenance-en\/\">predictive maintenance<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/remaining-useful-life-en\/\">remaining useful life<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/wear-en\/\">wear<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Approach%20to%20the%20Condition%20Description%20of%20Technical%20Components - https:\/\/industry-science.com\/en\/articles\/approach-to-the-condition-description-of-technical-components\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/articles\/approach-to-the-condition-description-of-technical-components\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip facebook\" title=\"Share on Facebook\" aria-label=\"Share on Facebook\" rel=\"noopener nofollow\"><i class=\"icon-facebook\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/x.com\/share?url=https:\/\/industry-science.com\/en\/articles\/approach-to-the-condition-description-of-technical-components\/\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip x\" title=\"Share on X\" aria-label=\"Share on X\" rel=\"noopener nofollow\"><i class=\"icon-x\" aria-hidden=\"true\"><\/i><\/a><a href=\"mailto:?subject=Approach%20to%20the%20Condition%20Description%20of%20Technical%20Components&body=Check%20this%20out%3A%20https%3A%2F%2Findustry-science.com%2Fen%2Farticles%2Fapproach-to-the-condition-description-of-technical-components%2F\" class=\"icon button circle is-outline tooltip email\" title=\"Email to a Friend\" aria-label=\"Email to a Friend\" rel=\"nofollow\"><i class=\"icon-envelop\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https:\/\/industry-science.com\/en\/articles\/approach-to-the-condition-description-of-technical-components\/&title=Approach%20to%20the%20Condition%20Description%20of%20Technical%20Components\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n","protected":false},"excerpt":{"rendered":"<p>This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.<\/p>\n","protected":false},"featured_media":96015,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80077,79574,80078,80079,80080,80081],"product_cat":[],"topic":[],"technology":[67790,67946,67634],"knowhow":[],"industry":[],"writer":[83308,80795,83307,83309],"content-type":[],"potential":[],"solution":[67678],"glossary":[],"class_list":{"0":"post-94179","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"category-design-en","7":"category-translate-en","8":"category-typeset","9":"tag-load-cycles-en","10":"tag-machine-learning-en","11":"tag-prediction-model-en","12":"tag-predictive-maintenance-en","13":"tag-remaining-useful-life-en","14":"tag-wear-en","15":"technology-artificial-intelligence","16":"technology-sensors","17":"technology-tools","18":"writer-anton-zitnikov-en","19":"writer-klaus-dieter-thoben-en","20":"writer-lukas-egbert-en","21":"writer-thorsten-tietjen-en","22":"solution-maintenance","23":"product","24":"first","25":"instock","26":"downloadable","27":"virtual","28":"sold-individually","29":"taxable","30":"purchasable","31":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansatz-zur-Zustandsbeschreibung-technischer-Bauteile-IM_2021_2-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/egbert-Ansat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Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94179","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\/96015"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94179"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94179"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94179"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94179"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94179"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94179"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94179"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94179"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94179"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94179"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94179"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94179"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94179"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}