{"id":4365,"date":"2022-05-24T14:55:19","date_gmt":"2022-05-24T06:55:19","guid":{"rendered":"https:\/\/ascendas-asia.com\/?page_id=4365"},"modified":"2024-12-12T11:24:23","modified_gmt":"2024-12-12T03:24:23","slug":"condition-monitoring","status":"publish","type":"page","link":"https:\/\/ascendas-asia.com\/vi\/resources\/condition-monitoring\/","title":{"rendered":"Condition Monitoring"},"content":{"rendered":"<h2 class=\"h3 add_font_color_orange\"><span style=\"color: #e67e23;\">Analyze sensor data to evaluate equipment health during operation<\/span><\/h2>\n<div>\n<div class=\"mainParsys parsys containsResourceName resourceClass-parsys\">\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<p>Condition monitoring is the process of collecting and analyzing sensor data from equipment in order to evaluate its health state during operation. Accurately identifying the current health state of equipment is critical to the development of&nbsp;<a href=\"https:\/\/uk.mathworks.com\/discovery\/predictive-maintenance-matlab.html\">predictive maintenance<\/a><span>&nbsp;<\/span>programs.<\/p>\n<p>Condition monitoring enables equipment manufacturers and operators to do the following:<\/p>\n<ul>\n<li>Reduce unplanned failures by identifying anomalies or faults before they become major problems<\/li>\n<li>Avoid the costs of unnecessary maintenance by scheduling equipment service only when necessary<\/li>\n<li>Reduce downtime by pinpointing the source of faults more quickly<\/li>\n<\/ul>\n<p>Condition monitoring is not only about collecting data but also using that data to evaluate the condition of a machine. This evaluation could be anything from a&nbsp;<a href=\"https:\/\/uk.mathworks.com\/help\/stats\/control-charts.html\">control chart<\/a><span>&nbsp;<\/span>that ensures a single sensor value does not exceed a safety threshold to a machine learning algorithm trained on hundreds of sensors with months of historical data, such as the one developed by<span>&nbsp;<\/span><a href=\"https:\/\/ascendas-asia.com\/vi\/mondi-pdm-using-machine-learning\/\">Mondi Gronau<\/a>.<\/p>\n<h3>&nbsp;<\/h3>\n<h4 style=\"font-weight: bold;\">Condition Monitoring vs. Prognostics<\/h4>\n<p>A predictive maintenance program may use both condition monitoring and prognostics algorithms. The main difference between condition monitoring and&nbsp;<a href=\"https:\/\/uk.mathworks.com\/discovery\/prognostics.html\">prognostics<\/a><span>&nbsp;<\/span>is the timeframe.<\/p>\n<\/div>\n<\/div>\n<div class=\"table resourceClass-table containsResourceName section\">\n<div>\n<table class=\"table table_100 table-bordered\" cellspacing=\"0\" style=\"width: 55.8864%;\">\n<tbody>\n<tr>\n<th style=\"padding: 1px; text-align: left; width: 28.9361%;\">&nbsp;<\/th>\n<th style=\"padding: 1px; width: 28.1964%; text-align: center;\">Timeframe<\/th>\n<th style=\"padding: 1px; width: 42.8398%; text-align: left;\">Example<\/th>\n<\/tr>\n<tr>\n<th style=\"padding: 1px; text-align: left; width: 28.9361%;\">Condition Monitoring<\/th>\n<td style=\"padding: 1px; width: 28.1964%; text-align: center;\">Current state<\/td>\n<td style=\"padding: 1px; width: 42.8398%; text-align: left;\"><a href=\"https:\/\/uk.mathworks.com\/help\/predmaint\/ug\/Rolling-Element-Bearing-Fault-Diagnosis.html\">Detecting faults in bearings<\/a><\/td>\n<\/tr>\n<tr>\n<th style=\"padding: 1px; text-align: left; width: 28.9361%;\">Prognostics<\/th>\n<td style=\"padding: 1px; width: 28.1964%; text-align: center;\">Future state<\/td>\n<td style=\"padding: 1px; width: 42.8398%; text-align: left;\"><a href=\"https:\/\/uk.mathworks.com\/help\/predmaint\/ug\/similarity-based-remaining-useful-life-estimation.html\">Estimating the remaining useful life of an aircraft engine<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<h2>&nbsp;<\/h2>\n<h3 style=\"font-weight: bold;\"><span style=\"color: #e67e23;\">Developing Condition Monitoring Algorithms in MATLAB<\/span><\/h3>\n<\/div>\n<\/div>\n<div class=\"cqImage containsResourceName section resourceClass-image\">\n<div class=\"clearfix mw-image thumbnail\">\n<div>\n<div>\n<p>A typical workflow for developing condition monitoring algorithms in MATLAB<sup>\u00ae<\/sup><span>&nbsp;<\/span>includes acquiring and preprocessing data, identifying condition indicators, training the model, and deploying and integrating the algorithm.<\/p>\n<\/div>\n<\/div>\n<div>\n<div>\n<div>\n<div>\n<div>\n<div>&nbsp;<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"clearfix mw-image thumbnail\"><img decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/condition-monitoring\/_jcr_content\/mainParsys\/band\/mainParsys\/lockedsubnav\/mainParsys\/columns\/0040b481-5422-43eb-b94f-2db26626c8dc\/image.adapt.full.medium.png\/1721885602328.png\" alt=\"Condition monitoring workflow diagram showing steps from acquiring data to deployment and integration.\" loading=\"lazy\" style=\"margin-left: auto; margin-right: auto; display: block;\" width=\"624\" height=\"199\"><\/p>\n<div class=\"caption\">\n<p style=\"text-align: center;\">Condition monitoring algorithm development workflow.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<h3>&nbsp;<\/h3>\n<h4 style=\"font-weight: bold;\">Acquire Data<\/h4>\n<p>To develop condition monitoring algorithms in MATLAB<sup>\u00ae<\/sup>, you need to start with data from your asset. Acquire data directly from sensors and<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/solutions\/test-measurement.html\">test hardware<\/a><span>&nbsp;<\/span>using<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/hardware-support\/home.html\">hardware support packages<\/a><span>&nbsp;<\/span>in MATLAB. Or, access streaming and archived data from services such as<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/discovery\/opc-ua.html\">OPC UA<\/a>,&nbsp;<a href=\"https:\/\/uk.mathworks.com\/help\/matlab\/ref\/webread.html\">RESTful web services<\/a>,<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/products\/database.html\">databases<\/a>,<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/help\/matlab\/import_export\/work-with-remote-data.html#bvn_hcu-2\">AWS S3<\/a>, and<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/help\/matlab\/import_export\/work-with-remote-data.html#mw_11daa39e-c1f6-475d-927b-87dc94718b99\">Azure Blob<\/a>.<\/p>\n<p>If you don\u2019t have enough data, you can also<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/help\/predmaint\/ug\/multi-class-fault-detection-using-simulated-data.html\">generate synthetic data<\/a><span>&nbsp;<\/span>by building a physical model of your asset.<\/p>\n<h3>&nbsp;<\/h3>\n<h4 style=\"font-weight: bold;\">Explore and Preprocess Data<\/h4>\n<p>Start by<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/help\/predmaint\/ug\/data-preprocessing-for-condition-monitoring-and-predictive-maintenance.html\">preprocessing<\/a><span>&nbsp;<\/span>and visualizing your data. Are you able to easily detect anomalies by eye? If so, you may be able to use a simple algorithm such as<span>&nbsp;<\/span><code><a href=\"https:\/\/uk.mathworks.com\/help\/signal\/ref\/findchangepts.html\">findchangepts<\/a><\/code><span>&nbsp;<\/span>or a<span>&nbsp;<\/span><code><a href=\"https:\/\/uk.mathworks.com\/help\/stats\/controlchart.html\">controlchart<\/a><\/code>.<\/p>\n<\/div>\n<\/div>\n<div class=\"cqImage containsResourceName section resourceClass-image\">\n<div class=\"clearfix mw-image thumbnail\"><img decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/condition-monitoring\/_jcr_content\/mainParsys\/band\/mainParsys\/lockedsubnav\/mainParsys\/columns_copy\/0040b481-5422-43eb-b94f-2db26626c8dc\/image.adapt.full.medium.png\/1721885602448.png\" alt=\"A MATLAB plot of motor voltage, fan speed, and temperature data from a cooling fan, showing anomalies that are easy to spot.\" loading=\"lazy\" style=\"margin-left: auto; margin-right: auto; display: block;\" width=\"531\" height=\"333\"><\/p>\n<div class=\"caption\">\n<p style=\"text-align: center;\">Sometimes anomalies are easy to spot in sensor readings, as shown in this MATLAB plot. In this case, a simple algorithm would suffice. <span style=\"font-size: 1rem;\">If your data contains many sensors, or anomalies are difficult to identify, you will need to explore more advanced techniques, such as machine learning and deep learning, to discover patterns in your data.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<h3>&nbsp;<\/h3>\n<h4 style=\"font-weight: bold;\">Develop Condition Monitoring Algorithms<\/h4>\n<p>To develop condition monitoring algorithms, you first need to identify condition indicators: features that indicate the difference between normal and faulty operation. These may be easy to spot or may require extracting and combining many features. The&nbsp;<a href=\"https:\/\/uk.mathworks.com\/help\/predmaint\/ref\/diagnosticfeaturedesigner-app.html\">Diagnostic Feature Designer<\/a><span>&nbsp;<\/span>app from&nbsp;<a href=\"https:\/\/uk.mathworks.com\/products\/predictive-maintenance.html\">Predictive Maintenance Toolbox\u2122<\/a><span>&nbsp;<\/span>lets you interactively extract, rank, and export a variety of features.<\/p>\n<\/div>\n<\/div>\n<div class=\"cqImage containsResourceName section resourceClass-image\">\n<div class=\"clearfix mw-image thumbnail\" style=\"padding-left: 40px;\"><img decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/condition-monitoring\/_jcr_content\/mainParsys\/band\/mainParsys\/lockedsubnav\/mainParsys\/columns_copy_1308338\/0040b481-5422-43eb-b94f-2db26626c8dc\/image_697792625.adapt.full.medium.jpg\/1721885602496.jpg\" alt=\"Training condition monitoring algorithms using the Diagnostic Feature Designer app to show pump flow rate features ranked by importance..\" loading=\"lazy\" style=\"margin-left: auto; margin-right: auto; display: block;\" width=\"1000\" height=\"562\"><\/p>\n<div class=\"caption\">\n<p style=\"text-align: center;\">With the Diagnostic Feature Designer app, you can interactively extract features to train condition monitoring algorithms.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<p>You can then use these features as inputs to machine learning or deep learning algorithms to train your condition monitoring algorithm. For example, you can interactively test a variety of fault classification algorithms using the&nbsp;<a href=\"https:\/\/uk.mathworks.com\/help\/stats\/classificationlearner-app.html\">Classification Learner<\/a><span>&nbsp;<\/span>app.<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<div class=\"cqImage containsResourceName section resourceClass-image\">\n<div class=\"clearfix mw-image thumbnail\" style=\"padding-left: 40px;\"><img decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/condition-monitoring\/_jcr_content\/mainParsys\/band\/mainParsys\/lockedsubnav\/mainParsys\/columns_copy_1308338_1803553766\/0040b481-5422-43eb-b94f-2db26626c8dc\/image.adapt.full.medium.png\/1721885602538.png\" alt=\"Screenshot of the Classification Learner app showing a confusion matrix of results from a trained machine learning algorithm.\" loading=\"lazy\" style=\"margin-left: auto; margin-right: auto; display: block;\" width=\"624\" height=\"401\"><\/div>\n<div class=\"clearfix mw-image thumbnail\" style=\"padding-left: 40px;\">&nbsp;<\/div>\n<div class=\"clearfix mw-image thumbnail\" style=\"padding-left: 40px; text-align: center;\"><span style=\"font-size: 1rem;\">With the Classification Learner app, you train a variety of classification models to use for classifying faults in condition monitoring.<\/span><\/div>\n<\/div>\n<div class=\"text containsResourceName section resourceClass-text\">\n<div class=\"mw-text\">\n<h3>&nbsp;<\/h3>\n<h4 style=\"font-weight: bold;\">Deploy and Integrate<\/h4>\n<p>Once validated, condition monitoring algorithms need to be operationalized in an&nbsp;<a href=\"https:\/\/uk.mathworks.com\/solutions\/enterprise-it-systems.html\" data-link=\"lead\" data-offertype=\"Web Trial Request for Enterprise and IT Systems\" data-component=\"text\">IT environment<\/a><span>&nbsp;<\/span>such as a server or cloud. Condition monitoring algorithms can also be deployed to an<span>&nbsp;<\/span><a href=\"https:\/\/uk.mathworks.com\/solutions\/embedded-code-generation.html\" data-link=\"lead\" data-offertype=\"Web Trial Request for Embedded Code Gen\" data-component=\"text\">embedded system<\/a>, enabling faster response times and significantly reducing the amount of data sent over the network.<\/p>\n<h3>&nbsp;<\/h3>\n<h2 style=\"font-weight: bold;\"><span style=\"color: #e67e23;\">Key Points<\/span><\/h2>\n<ul>\n<li>Condition monitoring can help you evaluate the health state of equipment during operation.<\/li>\n<li>These algorithms can vary from simple thresholding to complex machine learning and deep learning algorithms.<\/li>\n<li>MATLAB can help you develop and deploy condition monitoring algorithms. For additional information, see<a href=\"https:\/\/uk.mathworks.com\/products\/predictive-maintenance.html\">&nbsp;<\/a><a href=\"https:\/\/www.mathworks.com\/products\/predictive-maintenance.html\">Predictive Maintenance Toolbox<\/a><a href=\"https:\/\/uk.mathworks.com\/products\/predictive-maintenance.html\"><span>.<\/span><\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div>&nbsp;<\/div>\n<h3 style=\"text-align: justify;\">&nbsp;<\/h3>\n<h3>&nbsp;<\/h3>\n<p style=\"text-align: center;\"><a class=\"maxbutton-4 maxbutton maxbutton-download-a-free-trial\" target=\"_blank\" rel=\"noopener\" href=\"https:\/\/ascendas-asia.com\/vi\/matlab-trial-for-predictive-maintenance\/\"><span class='mb-text'>Download a FREE Trial<\/span><\/a>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<a class=\"maxbutton-1 maxbutton maxbutton-get-quote\" target=\"_blank\" rel=\"noopener\" href=\"https:\/\/ascendas-asia.com\/vi\/company\/#contact-us\"><span class='mb-text'>Request Consultation<\/span><\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Analyze sensor data to evaluate equipment health during operation Condition monitoring is the process of collecting and analyzing sensor data from equipment in order to evaluate its health state during operation. Accurately identifying the current health state of equipment is critical to the development of&nbsp;predictive maintenance&nbsp;programs. Condition monitoring enables equipment manufacturers and operators to do [&hellip;]<\/p>","protected":false},"author":4,"featured_media":0,"parent":18,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-4365","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.1 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Condition Monitoring - TechSource Systems &amp; Ascendas Systems Group<\/title>\n<meta name=\"description\" content=\"Condition monitoring is the process of collecting and analyzing sensor data from equipment in order to evaluate its health state\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ascendas-asia.com\/vi\/resources\/condition-monitoring\/\" \/>\n<meta property=\"og:locale\" content=\"vi_VN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Condition Monitoring\" \/>\n<meta property=\"og:description\" content=\"Condition monitoring is the process of collecting and analyzing sensor data from equipment in order to evaluate its health state\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ascendas-asia.com\/vi\/resources\/condition-monitoring\/\" \/>\n<meta property=\"og:site_name\" content=\"TechSource Systems &amp; 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