{"id":45932,"date":"2020-06-09T11:09:05","date_gmt":"2020-06-09T18:09:05","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45932"},"modified":"2025-03-31T11:12:35","modified_gmt":"2025-03-31T18:12:35","slug":"template-3","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/mechanical-systems-condition-monitoring-the-intersection-of-machine-learning-and-real-world-experience","title":{"rendered":"Mechanical System Condition Monitoring"},"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 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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-45932'><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>Mechanical System Conditioning Monitoring: The Intersection of Machine Learning and Real-World Experience<\/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\" >9. June 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-5c4a2bc0544fd3b85010be5f6c6fd56f\">\n.avia-image-container.av-14x5t1q-5c4a2bc0544fd3b85010be5f6c6fd56f img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-5c4a2bc0544fd3b85010be5f6c6fd56f .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-5c4a2bc0544fd3b85010be5f6c6fd56f 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-45933 avia-img-lazy-loading-not-45933 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1-1030x361.png\" alt='' title='Mech-Systems-CM-banner-1'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/Mech-Systems-CM-banner-1.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>The long-standing best practice of monitoring asset conditions for early signs of failure has saved industrial organizations untold millions of dollars. Its longevity is a testament to its success in helping to avoid unplanned downtime and improve safety, productivity, and operational performance. What is remarkable is how the once-manual exercise has evolved into an automated, digital, science-based practice.<\/p>\n<p>Today, human and machine intelligence coexist and grow with modern condition monitoring technology. Sophisticated analytics powered by machine learning (ML), intuitively applied by skilled workers, are propelling asset reliability, availability, and uptime to higher levels.<\/p>\n<p><strong>Journey to excellence<\/strong><\/p>\n<p>Fewer of us remember when machine condition monitoring was limited to technicians with deep equipment and process expertise. Through their eyes, ears, nose, or hands, they could sense when something was awry and search their memories, P&amp;ID schematics, trend charts, and historical records to diagnose and correct impending problems.<\/p>\n<p>Recognizing the immense value of the practice and the risks of relying on pockets of expertise, tools began rolling out to sharpen condition monitoring and analysis. Route-based inspections using techniques such as vibration analysis, oil analysis, and infrared thermography became the norm, and software and data historians were implemented to encapsulate the information.<\/p>\n<p>Now, in the era of the industrial internet of things (IIoT), cloud- and network-connected assets have smart sensors automatically harvesting condition data and fueling cutting-edge analytics and diagnostics. It is here where human and machine intelligence come together to elevate predictive maintenance, enable prescriptive maintenance, and optimize business outcomes.<\/p>\n<p>Tignis harnesses this capability in our physics-driven approach to monitoring the condition of connected mechanical systems. It is not enough to monitor individual assets; automated, continuous system monitoring enables unprecedented reliability and efficiency improvements.<\/p>\n<p>Using ML algorithms and digital twins, Tignis analyzes mechanical system connectivity; monitors changes in physical properties such as temperature, pressure, or water volumes; weighs the significance within the operational systems; and offers detailed recommendations. Because it applies the physics of flow \u2014 whether of fluid, water, electricity, mechanical energy, or heat \u2013 it increases the speed and accuracy of fault detection, reduces false positives, and delivers unique insights into anomalies that help with diagnostics, root cause analysis, and decision-making.<\/p>\n<p><strong>Why blending human and machine learning matters<\/strong><\/p>\n<p>ML algorithms and associated data continually self-improve by design. For this reason, some users may perceive the technology to be a threat to their job. In reality, the roles are complementary, and the human element is indispensable. The intelligent analytics must be successfully applied, and that requires skilled workers with a penchant for continuous learning.<\/p>\n<p>Tignis facilitates learning. Visualization of ML analytics in a digital twin allows users to rapidly recognize where and why degradation exists in the mechanical system, process, or operation. They can drill down for further details, compare current and historical IIoT sensor data, and quickly define and execute the best course of action to eliminate or mitigate the issue.<\/p>\n<p>Learning is accelerated with cloud-enabled remote monitoring and automated alerts providing real-time visibility for authorized operators, technicians, reliability engineers, or other concerned users \u2013 no matter where they are located. Collaboration capabilities allow internal teams to work together on issues and consult with Tignis data scientists or subject matter experts when needed.<\/p>\n<p>There is no better way to show how much you value your personnel than to enrich their expertise and facilitate continual learning. Their success is your success.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45933,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45932","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>Mechanical System Condition Monitoring - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"Sophisticated analytics powered by ML, intuitively applied by skilled workers, are propelling asset reliability, availability, and uptime to higher levels.\" \/>\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\/mechanical-systems-condition-monitoring-the-intersection-of-machine-learning-and-real-world-experience\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mechanical System Condition Monitoring - 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