{"id":3354,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3354"},"modified":"2022-07-26T13:06:17","modified_gmt":"2022-07-26T11:06:17","slug":"a-domain-knowledge-based-approach-for-fault-diagnosis","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/machine-tools-special-machines\/a-domain-knowledge-based-approach-for-fault-diagnosis\/","title":{"rendered":"A Domain Knowledge-based Approach for Fault Diagnosis"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Markus Netzer, Philipp Alexander, Tobias Schlagenhauf, Juergen Fleischer (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\">Complex dynamics of change caused by fluctuating production conditions require an efficient use of the industrial knowledge base for condition monitoring in machine tools. Data-based AI approaches enable the detection and classification of faults like blowholes and highly rely on the quantity and quality of the available process data. Due to the findings of the state of art, the use of digitalized domain knowledge for fault diagnosis is given too little attention. Based on various knowledge-based approaches for fault diagnosis a framework is presented that enables the formalization of implicit knowledge to improve the classification quality under parallel use of existing AI-approaches.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Fault classification; Anomaly classification; Fault diagnosis; Manufacturing; Knowledge-based approach; Knowledge formalization<\/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=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Markus_Netzer.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-background\" style=\"background-color:#f3f4f5\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"146\" class=\"wp-image-3864\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Philipp_Alexander_Photo.jpg?resize=150%2C146&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Philipp_Alexander_Photo.jpg?w=465&amp;ssl=1 465w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Philipp_Alexander_Photo.jpg?resize=300%2C292&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Philipp_Alexander_Photo.jpg?resize=150%2C146&amp;ssl=1 150w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Philipp Alexander<br><br>Karlsruhe Institute of Technology, Germany<br><br>markus.netzer@kit.edu<\/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 Markus Netzer, Philipp Alexander, Tobias Schlagenhauf, Juergen Fleischer (Germany) Abstract Complex dynamics of change caused by fluctuating production conditions require an efficient use of the industrial knowledge base for condition monitoring in machine tools. Data-based AI approaches enable the detection and classification of faults like blowholes and highly rely&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/machine-tools-special-machines\/a-domain-knowledge-based-approach-for-fault-diagnosis\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":296,"featured_media":0,"parent":3579,"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-3354","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3354","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\/296"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3354"}],"version-history":[{"count":3,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3354\/revisions"}],"predecessor-version":[{"id":3865,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3354\/revisions\/3865"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3579"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}