{"id":1980,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=1980"},"modified":"2021-07-14T09:06:11","modified_gmt":"2021-07-14T07:06:11","slug":"extended-kernel-density-estimation-for-anomaly-detection-in-streaming-data","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/machine-tools-anomaly-detection\/extended-kernel-density-estimation-for-anomaly-detection-in-streaming-data\/","title":{"rendered":"Extended kernel density estimation for anomaly detection in streaming data"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by <em>Julia Rosenberger, Kevin Mueller, Andreas Selig, Michael Buehren, Dieter Schramm<\/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\">Machine Learning algorithms based on kernel density estimation (KDE) are said to be well suited for anomaly detection. However, existing approaches mainly cover point anomaly detection. Many industrial applications also require detecting drifts, contextual and collective anomalies. Due to the demand for small latencies, edge computing gains significance and hybrid models are no adequate solution. The main contribution is the EEM-KDE algorithm, which includes extensions of the KDE for detecting all aforementioned types of anomalies in streaming data in the edge. Finally, the proposed algorithm is evaluated showing the capability to detect different types of anomalies using an industrial control unit.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Anomaly detection, Density estimation, Streaming data, Edge computing, Machine learning, Sensor signal<\/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\/Julia_Rosenberger.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=\"115\" height=\"173\" class=\"wp-image-2652\" style=\"width: 115px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Julia_Rosenberger_Photo.jpg?resize=115%2C173\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Julia_Rosenberger_Photo.jpg?w=304&amp;ssl=1 304w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Julia_Rosenberger_Photo.jpg?resize=200%2C300&amp;ssl=1 200w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/Julia_Rosenberger_Photo.jpg?resize=100%2C150&amp;ssl=1 100w\" sizes=\"auto, (max-width: 115px) 100vw, 115px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Julia Rosenberger<br><br>Bosch Rexroth AG, Germany<br><br>julia.rosenberger@boschrexroth.de<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>by Julia Rosenberger, Kevin Mueller, Andreas Selig, Michael Buehren, Dieter Schramm (Germany) Abstract Machine Learning algorithms based on kernel density estimation (KDE) are said to be well suited for anomaly detection. However, existing approaches mainly cover point anomaly detection. Many industrial applications also require detecting drifts, contextual and collective anomalies&#8230;.<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/machine-tools-anomaly-detection\/extended-kernel-density-estimation-for-anomaly-detection-in-streaming-data\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":4,"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-1980","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1980","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\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=1980"}],"version-history":[{"count":3,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1980\/revisions"}],"predecessor-version":[{"id":2653,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/1980\/revisions\/2653"}],"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=1980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}