{"id":1601,"date":"2025-09-17T10:16:05","date_gmt":"2025-09-17T03:16:05","guid":{"rendered":"https:\/\/www.its.ac.id\/publikasi\/?p=1601"},"modified":"2025-09-17T10:16:05","modified_gmt":"2025-09-17T03:16:05","slug":"predicting-failure-using-machine-learning-and-statistical-based-method-a-production-machine-case-study","status":"publish","type":"post","link":"https:\/\/www.its.ac.id\/publikasi\/2025\/09\/17\/predicting-failure-using-machine-learning-and-statistical-based-method-a-production-machine-case-study\/","title":{"rendered":"Predicting Failure using Machine Learning and Statistical Based Method: a Production Machine Case Study"},"content":{"rendered":"<div id=\"authorString\"><em>Effi Latiffianti, Stefanus Eko Wiratno, Samuel Aditya Christianta<\/em><\/div>\n<p>&nbsp;<\/p>\n<div id=\"articleAbstract\">\n<h4>Abstract<\/h4>\n<div>\n<p>This research investigates the applicability of failure detection models based on machine learning and statistical approaches to reduce unplanned downtime in a food production company. Sensor data is utilized to for identifying early failure symptoms. To capture temporal and sequential dependencies in time-series data, we employ one of potential network based method so called the Long Short Term Memory (LSTM) Autoencoder. Furthermore, we contrast the performance of the result with the traditional statistical method, the multivariate Exponentially Weighted Moving Average (EWMA). While both models successfully detected all failures, LSTM-AE demonstrated superior performance by reducing false alarms and providing true alarms with a longer time-to-failure. The findings highlight the potential of leveraging limited data for failure prediction, demonstrating the effectiveness of both models in detecting anomalies while emphasizing their role in enhancing productivity through early failure detection.<\/p>\n<\/div>\n<\/div>\n<div id=\"articleSubject\">\n<h4>Keywords<\/h4>\n<div>Anomaly detection; failure; fault; Long Short Term Memory Autoencoder; Multivariate Exponentially Weighted Moving Average<\/div>\n<div><a href=\"https:\/\/iptek.its.ac.id\/index.php\/jts\/article\/view\/22501\">PDF<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Effi Latiffianti, Stefanus Eko Wiratno, Samuel Aditya Christianta &nbsp; Abstract This research investigates the applicability of failure detection models based on machine learning and statistical approaches to reduce unplanned downtime in a food production company. Sensor data is utilized to for identifying early failure symptoms. To capture temporal and sequential dependencies in time-series data, we [&hellip;]<\/p>\n","protected":false},"author":250,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[85],"tags":[],"class_list":["post-1601","post","type-post","status-publish","format-standard","hentry","category-jts-jurnal"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predicting Failure using Machine Learning and Statistical Based Method: a Production Machine Case Study - IJC ( ITS Journal Center )<\/title>\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.its.ac.id\/publikasi\/2025\/09\/17\/predicting-failure-using-machine-learning-and-statistical-based-method-a-production-machine-case-study\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting Failure using Machine Learning and Statistical Based Method: a Production Machine Case Study - IJC ( ITS Journal Center )\" \/>\n<meta property=\"og:description\" content=\"Effi Latiffianti, Stefanus Eko Wiratno, Samuel Aditya Christianta &nbsp; Abstract This research investigates the applicability of failure detection models based on machine learning and statistical approaches to reduce unplanned downtime in a food production company. 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