{"id":3408,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3408"},"modified":"2022-07-26T13:07:29","modified_gmt":"2022-07-26T11:07:29","slug":"web-based-maintenance-work-support-by-neural-networks-d-detection-and-wear-estimation-of-components-in-wind-energy-turbines","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/web-based-maintenance-work-support-by-neural-networks-d-detection-and-wear-estimation-of-components-in-wind-energy-turbines\/","title":{"rendered":"Web based maintenance work support by neural networks &#8211; Detection and wear estimation of components in wind energy turbines"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Waldemar Zeitler, Moritz Quandt, Hendrik Stern, Michael Freitag (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\">Mobile assistance systems in demanding wind turbine maintenance processes can help to reduce the work time of individual tasks. One such task is the measurement of carbon brushes, which need replacement when mechanical abrasions occur. We developed an image-based detection of different types of carbon brushes using an autoencoder network. After the detection, we adjusted the alignment of each carbon brush to get the height to length ratio, without additional depth information, for a reasonable estimation of the mechanical abrasion and a suggestion for a possible replacement. This leads to a more efficient workflow and less manual work during maintenance procedures.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Computer vision; Orientation detection; Convolutional neural network; Quality control<\/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\/Waldemar_Zeitler.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=\"191\" class=\"wp-image-3965\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Zeitler.jpg?resize=150%2C191&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Zeitler.jpg?w=329&amp;ssl=1 329w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Zeitler.jpg?resize=236%2C300&amp;ssl=1 236w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Zeitler.jpg?resize=118%2C150&amp;ssl=1 118w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Waldemar Zeitler<br><br>University of Bremen, Germany<br><br>zei@biba.uni-bremen.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 Waldemar Zeitler, Moritz Quandt, Hendrik Stern, Michael Freitag (Germany) Abstract Mobile assistance systems in demanding wind turbine maintenance processes can help to reduce the work time of individual tasks. One such task is the measurement of carbon brushes, which need replacement when mechanical abrasions occur. We developed an image-based&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/web-based-maintenance-work-support-by-neural-networks-d-detection-and-wear-estimation-of-components-in-wind-energy-turbines\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":3668,"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-3408","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3408","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=3408"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3408\/revisions"}],"predecessor-version":[{"id":3966,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3408\/revisions\/3966"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3668"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}