{"id":45867,"date":"2020-09-08T12:59:29","date_gmt":"2020-09-08T19:59:29","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45867"},"modified":"2025-03-28T13:03:48","modified_gmt":"2025-03-28T20:03:48","slug":"detection-strategies","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/risk-and-fault-detection-strategies","title":{"rendered":"Risk and Fault Detection Strategies"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141\">\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-parallax .av-parallax-inner{\nbackground-repeat:no-repeat;\nbackground-image:url(\/wp-content\/uploads\/2025\/03\/resources-banner-1500x333.jpg);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-section-color-overlay{\nopacity:0.3;\nbackground-color:#000000;\n}\n<\/style>\n<div id='av_section_1'  class='avia-section av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 main_color avia-section-default avia-no-border-styling  avia-builder-el-0  el_before_av_one_full  avia-builder-el-first  av-parallax-section avia-bg-style-parallax av-section-color-overlay-active av-minimum-height av-minimum-height-25 av-height-25  container_wrap fullsize'  data-section-bg-repeat='no-repeat' data-av_minimum_height_pc='25' data-av_min_height_opt='25'><div class='av-parallax' data-avia-parallax-ratio='0.3' ><div class='av-parallax-inner main_color avia-full-stretch'><\/div><\/div><div class=\"av-section-color-overlay-wrap\"><div class=\"av-section-color-overlay\"><\/div><div class='container av-section-cont-open' ><main  role=\"main\" itemprop=\"mainContentOfPage\"  class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45867'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ig608p-4f8d1c0453663a84f522f5ebe321d648\">\n.flex_column.av-ig608p-4f8d1c0453663a84f522f5ebe321d648{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ig608p-4f8d1c0453663a84f522f5ebe321d648 av_two_third  avia-builder-el-1  el_before_av_one_third  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a\">\n#top .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a{\npadding-bottom:10px;\ncolor:#ffffff;\nfont-size:40px;\n}\nbody .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:40px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .special-heading-inner-border{\nborder-color:#ffffff;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:25px;\n}\n\n@media only screen and (min-width: 768px) and (max-width: 989px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:50px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:25px;\n}\n}\n\n@media only screen and (min-width: 480px) and (max-width: 767px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:47px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:20px;\n}\n}\n\n@media only screen and (max-width: 479px){ \n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag{\nfont-size:33px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:15px;\n}\n}\n<\/style>\n<div  class='av-special-heading av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a av-special-heading-h1 custom-color-heading blockquote modern-quote  avia-builder-el-2  avia-builder-el-no-sibling  av-inherit-size av-linked-heading'><h1 class='av-special-heading-tag '  itemprop=\"headline\"  >PHYSICS, MACHINES AND DATA<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2pyrzd-6af470dabd56f3b979785905ac98eceb\">\n.flex_column.av-2pyrzd-6af470dabd56f3b979785905ac98eceb{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-2pyrzd-6af470dabd56f3b979785905ac98eceb av_one_third  avia-builder-el-3  el_after_av_two_third  avia-builder-el-last  flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a\">\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a av-styling- avia-align-center  avia-builder-el-4  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45060 avia-img-lazy-loading-not-45060 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png\" alt='Tignis, A Cohu Analytics Solution logo in white' title='Tignis-Logo-cohu-tagline-all white-transparent'  height=\"89\" width=\"300\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png 300w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1030x305.png 1030w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-768x228.png 768w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1536x455.png 1536w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-2048x607.png 2048w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1500x444.png 1500w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-705x209.png 705w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/div><\/div><\/div><\/div>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45867'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-2y29t5-8eac002ebd25dfff14a3d2eed1a49041 av_one_full  avia-builder-el-5  el_after_av_section  el_before_av_post_metadata  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266\">\n#top .av_textblock_section.av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 .avia_textblock{\nfont-size:40px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><strong>Risk and Fault Detection Strategies<\/strong><\/p>\n<\/div><\/section><\/div><div  class='av-post-metadata-container av-1h9dbih-f3a0f8fb0dd601dbad28c4d31b098a86 av-metadata-container-align-center  avia-builder-el-7  el_after_av_one_full  el_before_av_hr  av-metadata-container-1'><div class='av-post-metadata-container-inner'><span class=\"av-post-metadata-content av-post-metadata-meta-content\"><span class=\"av-post-metadata-content av-post-metadata-published\"><span class=\"av-post-metadata-published-date\" >8. September 2020<\/span><\/span><span class=\"av-post-metadata-content av-post-metadata-separator\">|<\/span><span class=\"av-post-metadata-content av-post-metadata-category\"><span class=\"av-metadata-before av-metadata-before-categories\">in<\/span><span class=\"av-post-metadata-category-link\" ><a href=\"https:\/\/www.cohu.com\/category\/thought-leadership\/\" >Thought Leadership<\/a><\/span><\/span><\/span><\/div><\/div><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t25391-39260fa6e7d6022a5f36742c2f35864e\">\n#top .hr.hr-invisible.av-m8t25391-39260fa6e7d6022a5f36742c2f35864e{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8t25391-39260fa6e7d6022a5f36742c2f35864e hr-invisible  avia-builder-el-8  el_after_av_post_metadata  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-1ltc3hq-d678e11ab7342dc0fff705952edff81f av_one_full  avia-builder-el-9  el_after_av_hr  el_before_av_hr  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-14x5t1q-97c614382aebe28dc4badd287200ce05\">\n.avia-image-container.av-14x5t1q-97c614382aebe28dc4badd287200ce05 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-97c614382aebe28dc4badd287200ce05 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-97c614382aebe28dc4badd287200ce05 av-styling- avia-align-center  avia-builder-el-10  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45868 avia-img-lazy-loading-not-45868 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner-1030x361.png\" alt='' title='Risk-and-Fault-Detection-Strategies-blog-banner'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/Risk-and-Fault-Detection-Strategies-blog-banner.png 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48\">\n#top .hr.hr-invisible.av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48 hr-invisible  avia-builder-el-11  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div><\/p>\n<div  class='flex_column av-tc5sy6-c610ce4ce37ddc8755f7c103673cd0c6 av_one_full  avia-builder-el-12  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035\">\n#top .av_textblock_section.av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Operators of critical machines and systems have no time for downtime. Connected mechanical systems equipped with industrial internet of things (IIoT) sensors will constantly stream data about their condition in the hopes that someone will detect clues about their future state. Processing the massive volume of data and converting it into timely measures to extinguish threats is supported by condition monitoring and analytics solutions.<\/p>\n<p>The best solutions employ advanced risk and fault detection strategies that expedite analytics, diagnostics, and decision making. The Tignis solution contains four automated mechanisms for detecting risks and faults, each of which requires a different set of available information:<\/p>\n<ul>\n<li><strong>Anomaly detection:<\/strong>\u00a0When monitoring a system, the observed value of a given physical property is constantly compared to the predicted value, and if there is significant or sustained variation, it is marked as anomalous.\u00a0This may include flow, load, force, pressure, temperature, vibration, or other performance variables.<\/li>\n<\/ul>\n<p>Powered by ML, anomaly detection involves taking historical sensor data, picking a measurable outcome that matters (usually one sensor that measures a particularly important physical property), and then training an ML model to predict that property based on the measurements of other correlated properties across the system.<\/p>\n<p>Anomaly detection can be applied to any digital twin with sensor data, even when Tignis data scientists personally have no idea how the system or machine works before seeing it. This minimizes barriers to value.<\/p>\n<ul>\n<li><strong>Engineering principles and statistics:<\/strong>\u00a0Issues can be detected based on known engineering\/physics principles and statistics using Tignis\u2019 proprietary analytics query language. The digital-twin-aware query language sits somewhere conceptually between SQL and Excel, and it is designed to enable non-technical subject matter experts such as mechanical engineers to easily and quickly build rules about how a system with a given digital twin\u00a0<em>should<\/em>\u00a0Compared to anomaly detection, this mechanism can be more prescriptive about detected problems and possible solutions.<\/li>\n<\/ul>\n<p>Engineering principles require encoded engineering\/physics rules for at least one of the components in the system being modeled. Fortunately, many basic components are common everywhere, including pumps, fans, tanks, compressors, and more.<\/p>\n<p>Tignis works with each new customer to add new rules for the processes and assets they really care about, whether they are common or not. The efforts are facilitated by the increasingly large library of encoded engineering knowledge we are amassing.<\/p>\n<ul>\n<li><strong>Supervised machine learning:\u00a0<\/strong>The most effective ML develops when training examples are available; specifically, records of past occurrences of events that should be preemptively predicted and prevented in the future.\u00a0Supervised ML enables early detection of previously experienced issues. Think of this as training a machine to recognize the signature of an undesirable condition.<\/li>\n<\/ul>\n<p>Supervised ML requires well-documented and annotated digital records of past failures along with associated sensor data. That data often does not exist but is incredibly effective when it does.<\/p>\n<ul>\n<li><strong>Simulation:<\/strong>\u00a0Never-before-experienced issues can be detected early by combining supervised ML with simulation. If a high-fidelity simulator is available for a process, Tignis can use it to simulate any possible state of the system and generate training data, which is then used to train an ML early-detection algorithm. The ML detection algorithms are orders of magnitude faster than the simulation and can be practically applied in real time.<\/li>\n<\/ul>\n<p>High-fidelity simulators are not widely used in industry at the moment. They are most often reserved for the most risky or complicated plants or processes, such as nuclear plants or some hydrocarbon plants, because they can cost millions to build and will only apply to a single plant. That will change as Tignis has developed techniques and tools that make simulation more accessible. Tignis has world-class experts in chemical engineering, mechanical engineering, and physics who can build simulations for high-value processes. For example, Tignis recently built a simulator for the physics of one process step within semiconductor processing.<\/p>\n<p>Threats will become a reality if they remain unseen. Using an advanced condition monitoring and analytics solution with a multifaceted blend of automated threat detection mechanisms helps to expose equipment risks, enable predictive maintenance and reliability optimization measures, and keep the operation running as expected.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45868,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45867","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Risk and Fault Detection Strategies - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"Processing massive volume of data and converting it into timely measures to extinguish threats is supported by condition monitoring and analytics solutions.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cohu.com\/tignis\/risk-and-fault-detection-strategies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Risk and Fault Detection Strategies - 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