{"id":45914,"date":"2020-07-15T10:37:28","date_gmt":"2020-07-15T17:37:28","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45914"},"modified":"2025-03-31T11:03:53","modified_gmt":"2025-03-31T18:03:53","slug":"chiller-surge-counts","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/the-journey-of-a-data-scientist-chiller-surge-counts","title":{"rendered":"The Journey of a Data Scientist: Chiller Surge Counts"},"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 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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-45914'><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 Journey of a Data Scientist: Chiller Surge Counts<\/strong><\/p>\n<\/div><\/section><\/div>\n<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\" >15. July 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>\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-e25cdcb4fb75d035a5a52890bc6f6f34\">\n.avia-image-container.av-14x5t1q-e25cdcb4fb75d035a5a52890bc6f6f34 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-e25cdcb4fb75d035a5a52890bc6f6f34 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-e25cdcb4fb75d035a5a52890bc6f6f34 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-45915 avia-img-lazy-loading-not-45915 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner-1030x361.png\" alt='' title='Chiller-Surge-blog-banner'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/Chiller-Surge-blog-banner.png 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n\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>\n<div  class='flex_column av-tc5sy6-c610ce4ce37ddc8755f7c103673cd0c6 av_one_full  avia-builder-el-12  el_after_av_hr  el_before_av_textblock  first flex_column_div  '     ><p>\n<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>Chillers vibrate routinely, surge occasionally, and excesses in both can be problematic. Distinguishing between benign and detrimental behavior is essential to ensuring successful chiller maintenance and protection.<\/p>\n<p>Refrigerant that reverses course and flows from the condenser to the compressor causes chillers to vibrate more than normal, creating a groaning or squealing sound. Excessive surge events, whether from poor maintenance or poor control of water flow rates or temperature, can reduce a chiller\u2019s reliability and life span.<\/p>\n<p>The first line of defense is often a surge detector that counts chiller surge events; however, uncleansed data leads to poor analysis and conclusions. Plots of raw, unfiltered vibration sensor data typically reveal monotonically increasing surge counts interspersed with spikes, dips, and plunges (Figure 1). False positives are not uncommon and decision making based on misleading data can be ineffective and costly. Even in simple tasks, such as alerting on a rapid increase of a counter, the devil is in the details.<\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-zpuum2-105abcb6363455e3244abe23ee9f612a\">\n.avia-image-container.av-zpuum2-105abcb6363455e3244abe23ee9f612a img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-zpuum2-105abcb6363455e3244abe23ee9f612a .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-zpuum2-105abcb6363455e3244abe23ee9f612a av-styling- avia-align-center  avia-builder-el-14  el_after_av_textblock  el_before_av_textblock '   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-45923 avia-img-lazy-loading-not-45923 avia_image ' src=\"\/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-1030x451.png\" alt='' title='Figure-1-Raw-surge-counter-2048x897'  height=\"451\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-1030x451.png 1030w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-300x131.png 300w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-768x336.png 768w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-1536x673.png 1536w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-1500x657.png 1500w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1-705x309.png 705w, \/wp-content\/uploads\/2020\/07\/Figure-1-Raw-surge-counter-2048x897-1.png 2048w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><br \/>\n<section  class='av_textblock_section av-m8xdm8r6-d3f4a26fe96eb5f8583800e2f1387903 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p class=\"elementor-heading-title elementor-size-default\"><em>Figure 1: <\/em><em>Raw surge counts reported by a chiller. Should be monotonically increasing. Bad data leads to false alarm.<\/em><\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8xct3o4-4a999b21c205553cd2e0aa429d434956\">\n#top .av_textblock_section.av-m8xct3o4-4a999b21c205553cd2e0aa429d434956 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8xct3o4-4a999b21c205553cd2e0aa429d434956 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Doing analysis with equipment data requires special expertise. With the right tools and skill sets, steps can be taken to cleanse the data, find the signals through the noise, and generate thresholds for actionable alarms based on meaningful surge information. Tignis helps companies apply data science in this manner to improve preventive and predictive maintenance. Tignis has developed a proprietary technology that enables rapid and agile integration, normalization, and analysis of physical equipment and associated sensor data.<\/p>\n<p><strong>Data science increases trust in the data<\/strong><\/p>\n<p>There are several reasons surge count data can be \u201cdirty\u201d and prone to misinterpretation:<\/p>\n<ul>\n<li>Sensors may register surges even when the chiller is off, whether due to maintenance or another environmental trigger.<\/li>\n<li>Surge counts may momentarily drop to zero due to a power or network communication failure, and then bounce back up to where the counter left off.<\/li>\n<li>Surge counts may reset to zero if a sensor loses power for too long, is replaced, or needs replacement, causing the counter to start over.<\/li>\n<li>Some sensors are more sensitive than others, e.g., one chiller\u2019s detector might register 10 surges while another registers one surge for a similar event.<\/li>\n<li>Overly sensitive detectors may produce false alarms when no surge actually occurred, and overactive or malfunctioning sensors may do so repeatedly.<\/li>\n<\/ul>\n<p>Rules, flags, and filters are cleansing techniques used by data scientists to produce useful information. Instead of relying on an absolute count of every detected chiller surge, data science allows plants to see actual changes over longer periods of time by taking moment-to-moment data (e.g., every five minutes) and comparing the current count to the previous count.<\/p>\n<p>Rules can flag when there are more events than expected so that suspicious values that clutter up relevant data can be dropped out. With filters, zero values from momentary drops or periods when the chiller was not actually running can be dropped, along with the negative values seen in full resets. Identifying and eliminating random spikes in the data helps to focus attention on clumps or clusters of spikes. These measures, which increase trust in the data, enable analysis of well-defined groups of activity for the overall number of surges over a given period of time.<\/p>\n<p>Once the data is cleaned and filtered, thresholds can be set to generate alerts. The goal for alarm thresholds is to generate alerts when the chiller has a surprising number of surges over a specified time frame. For instance, more than 15 surges in a six-hour period could be an event triggering an alert to the maintenance team (Figure 2).<\/p>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2ab38a-ebab6b117d4db1696b4fb5f125c462b7\">\n.avia-image-container.av-2ab38a-ebab6b117d4db1696b4fb5f125c462b7 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-2ab38a-ebab6b117d4db1696b4fb5f125c462b7 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-2ab38a-ebab6b117d4db1696b4fb5f125c462b7 av-styling- avia-align-center  avia-builder-el-17  el_after_av_textblock  avia-builder-el-last '   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-45924 avia-img-lazy-loading-not-45924 avia_image ' src=\"\/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-1030x452.png\" alt='' title='Figure-2-Surge-count-2048x898'  height=\"452\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-1030x452.png 1030w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-300x132.png 300w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-768x337.png 768w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-1536x674.png 1536w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-1500x658.png 1500w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1-705x309.png 705w, \/wp-content\/uploads\/2020\/07\/Figure-2-Surge-count-2048x898-1.png 2048w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/p><\/div><section  class='av_textblock_section av-av_textblock-564067357eb74c20cdd60b282c9b50bd '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p class=\"elementor-heading-title elementor-size-default\"><em>Figure 2:<\/em><em> Surge count over six-hour windows, after filtering bad data. But what\u2019s the right threshold on which to alarm? Different chillers need different thresholds.<\/em><\/p>\n<\/div><\/section><\/p>\n<section  class='av_textblock_section av-m8xdkw1e-3bdd33ba8581c052d40b24382c668590 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Using this analysis, a dynamic threshold can be assigned based on how many surge events for this chiller is normal over a period of time. This is accomplished by finding a high quantile over an historic period, such as the 98% quantile over the previous 30 days, and alerting on surges above that amount (Figure 3).<\/p>\n<\/div><\/section>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8xdlr5h-0103f7d090435872a3f4920b8bfb1a51\">\n.avia-image-container.av-m8xdlr5h-0103f7d090435872a3f4920b8bfb1a51 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8xdlr5h-0103f7d090435872a3f4920b8bfb1a51 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8xdlr5h-0103f7d090435872a3f4920b8bfb1a51 av-styling- avia-align-center  avia-builder-el-20  el_after_av_textblock  el_before_av_textblock '   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-45926 avia-img-lazy-loading-not-45926 avia_image ' src=\"\/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-1030x450.png\" alt='' title='Figure-3-Surges-2048x895'  height=\"450\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-1030x450.png 1030w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-300x131.png 300w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-768x336.png 768w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-1536x671.png 1536w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-1500x656.png 1500w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1-705x308.png 705w, \/wp-content\/uploads\/2020\/07\/Figure-3-Surges-2048x895-1.png 2048w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div>\n<section  class='av_textblock_section av-av_textblock-564067357eb74c20cdd60b282c9b50bd '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p class=\"elementor-heading-title elementor-size-default\"><em>Figure 3: Surges over six hours, with dynamic threshold unique to each chiller. Finally, an alert rule that generates the right amount of alerts across all chillers.<\/em><\/p>\n<\/div><\/section>\n<section  class='av_textblock_section av-m8xcu41u-f02f428d6fbd73d46551763f35b77ce3 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Dynamic alert thresholds are useful because they can be applied to chillers across the board. Due to variances in chiller design, sensor sensitivity, and operating conditions, the threshold for one chiller may not work well for another. Therefore, alerting thresholds must be selected individually for every chiller based on its own historic activity. Applying dynamic thresholds to the cleansed data is an effective longer-term solution.<\/p>\n<p><strong>Strong tooling and expertise make a difference<\/strong><\/p>\n<p>Most of the work in data science and machine learning (ML) is in cleaning the data and getting it ready to model. Having robust tooling and deep technical expertise in data analysis and transformation streamlines the process. For instance, Tignis developed an internal tool for data processing, exploration, and modeling of mechanical systems. It establishes a digital twin of the system where all connections and sensors are visualized and modeled, and a data processing layer on top where the rules, filters, and thresholds are formulated and deployed.<\/p>\n<p>Preventing the destructive effects of chiller surge is a must for any maintenance organization, and data science can facilitate an intelligent approach. Tignis is uniquely equipped to help.<\/p>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45915,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45914","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 Journey of a Data Scientist: Chiller Surge Counts - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"Data science can facilitate an intelligent approach to preventing the destructive effects of chiller surge to ensure successful chiller maintenance and protection.\" \/>\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-journey-of-a-data-scientist-chiller-surge-counts\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Journey of a Data Scientist: Chiller Surge Counts - 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