{"id":45981,"date":"2020-03-12T12:04:08","date_gmt":"2020-03-12T19:04:08","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45981"},"modified":"2025-03-31T12:07:04","modified_gmt":"2025-03-31T19:07:04","slug":"systems-analysis","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/the-3-essential-of-impactful-systems-analysis","title":{"rendered":"The 3 Essentials of Impactful Systems Analysis"},"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 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.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-45981'><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>The 3 Essentials of Impactful Systems Analysis<\/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\" >12. March 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-002f4c884b3c1e26678ab18bea2139b9\">\n.avia-image-container.av-14x5t1q-002f4c884b3c1e26678ab18bea2139b9 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-002f4c884b3c1e26678ab18bea2139b9 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-002f4c884b3c1e26678ab18bea2139b9 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-45982 avia-img-lazy-loading-not-45982 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Tignis-blog-2-banner-1030x361.png\" alt='' title='Tignis-blog-2-banner'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Tignis-blog-2-banner-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/Tignis-blog-2-banner-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/Tignis-blog-2-banner-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/Tignis-blog-2-banner-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/Tignis-blog-2-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>At Tignis, we help you increase the reliability of your connected mechanical systems so you can be smarter and more innovative in the ways you do your job. Our \u201csecret sauce\u201d is using physics-based machine learning algorithms along with a database replica of your physical environment\u2014a digital twin\u2014to yield concise analysis, tailored to your specific environment\u2019s needs.<\/p>\n<p>In future blog posts, we\u2019ll go into more detail about what all that means. For today, let\u2019s start out by looking at a few key ways you can modernize your asset monitoring processes to make them more reliable and efficient. These three focus areas are a best practice for any asset manager.<\/p>\n<h2>1. Make the most of your existing sensors<\/h2>\n<p>The internet of things (IoT) promises ultimate connectivity across all things in every business environment, including the individual parts of the mechanical systems you manage. This advent of 24\/7 monitoring capabilities encouraged facilities to equip mechanical systems with a broad range of sensors. As you know, using sensors can help extend your condition-based maintenance through early detection of events that might impact the reliability and efficiency of your systems.<\/p>\n<p>That\u2019s great\u2014it\u2019s certainly worth gathering relevant data about your systems\u2014but, even putting aside that word \u201crelevant\u201d for a minute (because not all data is relevant, after all) installing sensors takes time and money. There\u2019s not just the cost of the equipment itself, but also the infrastructure needed to make it useful, from wiring the electrical connections (which can be especially expensive in manufacturing and industrial environments) to the more high-tech components, such as the network and IoT software platform you use for collecting and analyzing the sensor data. There\u2019s also the time it takes just to engage your IT team and then figure out where the sensors actually need to go. And don\u2019t forget, fitting new sensors into any hard-to-reach places can pose life safety risks that take longer to mitigate and can run up your budget even further.<\/p>\n<p>The good news? If you have some automation in your current system, you already have a head start. With the right software, you can leverage the data collection capabilities of your existing sensors and apply physics-based machine learning models using a digital twin to yield the results you need. In many environments, you can use this approach to detect and manage a rich set of maintenance conditions with a surprisingly low number of sensors.<\/p>\n<p>By applying this technique and working with us to develop the right solution that meets their needs, our customers transform their condition monitoring and expand coverage while overcoming the typical limitations of cost, life safety, IT project churn, and uncertainty about new sensor placement. By calculating simple algorithms based on known physical principles, we help companies do away with extraneous sensor equipment in places where it\u2019s expensive and problematic to add and maintain.<\/p>\n<h2>2. Find faulty sensors more easily<\/h2>\n<p>Once you\u2019ve evaluated the sensors in your environment to see how you can do more with less, you need to make sure the sensors you have in place are continuing to add value over time. Assuming a given sensor is providing data that is useful to your systems analysis, you need to keep the sensor operational in ways that ensure that data is accurate.<\/p>\n<p>For most maintenance teams responding to a preventive action request, this entails lots of hands-on physical testing or cross-validation with other sensors. The reality is that most of us don\u2019t have time to do this work proactively, so we end up testing sensors only when we suspect a fault\u2014and that\u2019s usually when something much larger has already gone wrong.<\/p>\n<p>Again, the solution comes down to having the right data and knowing what to do with it. Just as physics-based calculations can help you determine where sensors are unnecessary, similar calculations can tell you whether the data being reported by a sensor is suspected of being inaccurate. Sometimes this is just a warning, and can clue you in to the need for proactive maintenance; and sometimes, when the physics just don\u2019t make sense, it\u2019s a clear sign that a sensor has failed\u2014or else there\u2019s a serious problem with your systems.<\/p>\n<p>As with #1 above, these calculations only work if you\u2019ve scripted your machine learning algorithms to apply basic physical laws, and you have previously mapped your entire constellation of system variables into a central database (digital twin).<\/p>\n<h2>3. Weed out the false positives<\/h2>\n<p>Having lots of sensors producing data and lots of system rules processing the data leads to lots of alerts going off, calling your attention to supposedly urgent conditions. Some of these are real, but many are not. It\u2019s no surprise to any seasoned asset manager that rules are fallible, and very often alerts come across whose business relevance is basically zero. At Tignis, we refer to these generally as false positives. We also just sometimes call them noise.<\/p>\n<p>Here are the three most typical responses to a false positive alert:<\/p>\n<ul>\n<li>\u201cThe alert is based on faulty data, which I might or might not recognize as faulty in the moment.\u201d<\/li>\n<li>\u201cThe alert is based on accurate data, but I\u2019m pretty sure it\u2019s reporting a condition that doesn\u2019t exist.\u201d<\/li>\n<li>\u201cThe data is accurate, and the condition exists\u2014but, for my business purposes, I just don\u2019t care.\u201d<\/li>\n<\/ul>\n<p>And actually, \u201cI don\u2019t care\u201d is really the logical response to all of these alerts. You shouldn\u2019t care, because you have more important things to focus on in your day. It\u2019s like having someone run into your office every five minutes with a new, urgent message that actually has no pertinence to the business, and all you want to do is do your work.<\/p>\n<p>You can say enough\u2019s enough, and shut and lock the door so that person can\u2019t run in any more. But then, what happens when they\u2019re no longer crying wolf, and there actually is a serious problem?<\/p>\n<p>Such is the danger of having too much \u201cnoise\u201d in your day\u2014it blurs the edges between the things you actually care about and the stuff you don\u2019t. By modernizing your asset monitoring systems to report only relevant alerts, you can be more intentional and clear-headed about the alerts you respond to, and more impactful in your job.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45982,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45981","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>The 3 Essentials of Impactful Systems Analysis - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"Physics-based machine learning algorithms with a digital twin to yield concise analysis, tailored to your specific environment\u2019s needs.\" \/>\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\/the-3-essential-of-impactful-systems-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The 3 Essentials of Impactful Systems Analysis - 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