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This study builds a machine learning-based PdM system for a computed tomography (CT) scan machine by leveraging IoT sensors measuring temperature, humidity, current, radiation, and XY-axis acceleration. Data from January to December 2023 were processed to represent normal operating conditions, and synthetic faulty-tail data were generated to address limited fault records. An ANN predicted breakdown risk with 97.58% accuracy and flagged high risk in November 2023, matching subsequent repair needs in early December.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/assessment-of-iot-driven-predictive-maintenance-strategies-for-computed-tomography-equipment-a-machine-learning-approach/291025/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/assessment-of-iot-driven-predictive-maintenance-strategies-for-computed-tomography-equipment-a-machine-learning-approach/291025.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What sensors and variables are used for the CT predictive maintenance model?","Question",{"text":113,"@type":114},"The study uses IoT sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration, which serve as inputs to the predictive model.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the study handle the limited availability of faulty-condition data?",{"text":118,"@type":114},"It preprocesses readings to follow a normal distribution and generates synthetic data by expanding the tails of the distribution and adding Gaussian noise to create fault-like samples.",{"name":120,"@type":111,"acceptedAnswer":121},"How accurate is the proposed machine learning approach, and what real-world check supports it?",{"text":122,"@type":114},"The ANN achieves 97.58% prediction accuracy and consistently predicts high breakdown risk in November 2023, which is confirmed by a repair report indicating maintenance was required in early December 2023.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291025,1789643201,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","IEEE RELIABILITY SOCIETY SECTION  \nReceived 21 November 2024, accepted 9 December 2024, date of publication 16 December 2024, date of current version 30 December 2024.  \nDigital Object Identifier 10.1109/ACCESS.2024.3518516  \nAssessment of IoT-Driven Predictive Maintenance Strategies for Computed Tomography Equipment: A Machine Learning Approach  \nMOHAMMAD HABIB SHAH ERSHAD MOHD AZRUL SHAZRIL1, SYAMSIAH MASHOHOR1,2, MOHD EFFENDI AMRAN3, NUR FATINAH HAFIZ1, AZIZI MOHD ALI 1,2,  \nMOHD SAIFUL BIN NASERI3, AND MOHD FADLEE A. RASID1,2  \n1Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia  \n2Wireless and Photonics Networks Research Centre (WiPNET), Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia  \n3Biomedical Sector, Engineering Service Division, Ministry of Health Malaysia, Putrajaya 62590, Malaysia Corresponding author: Syamsiah Mashohor ([syamsiah@upm.edu.my](syamsiah@upm.edu.my))  \nThis work was supported by the Ministry of Health Malaysia (MOH) under Project 1424 .  \nABSTRACT Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration. Data were collected from January to December 2023 at a hospital in the Klang Valley, Malaysia. The readings were preprocessed to follow a normal distribution, representing the typical working conditions of the machine. Owing to limited faulty condition data, synthetic data were generated by expanding the tails of the data distribution and using a Gaussian noise generator. These synthetic data are vital for training robust machine learning models. An artificial neural network (ANN) was designed to predict the machine’s breakdown risk using all sensor parameters as inputs. The ANN model achieved an impressive prediction accuracy of 97.58%, proving its relibility in forecasting breakdowns. The model consistently predicted a high breakdown risk in November 2023, which was confirmed by a repair report that indicating maintenance was required in early December 2023 . This study demonstrated that integrating IoT sensors with ANN models can significantly enhance the PdM of medical equipment, reduce downtime, and improve operational efficiency. These promising results suggest the potential application of this approach in other critical medical devices.  \nINDEX TERMS Predictive maintenance, CT-scan, artificial neural network, machine learning, synthetic data, IoT.  \nI. INTRODUCTION  \nThe medical industry relies heavily on high-end and complex equipment to provide patients with precise and reliable health care services. Efficient operation of this equipment is crucial for effective diagnosis and treatment. According to World Health Organization (WHO) statistics, over 80% of medical equipment failures are preventable, with inadequate maintenance responsible for approximately 60% of all performance issues [1] .  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Haidong Shao .  \nPredictive maintenance (PdM) identifies equipment conditions and forecasts when maintenance is required. The PdM aims to determine the optimal timing for maintenance actions by utilizing information about the system’s current health state and historical maintenance data [2] . In the health industry, Artificial Intelligence (AI) and the Internet of Things (IoT) have been adopted at many stages and across various medical diagnostics, as reviewed in [3] and [4] . This demonstrates the acceptance by medical experts, with AI-based systems being trusted to assist in decision-making. A case study conducted in [5] observed that Computed Tomography","cbCaipudpqUyGMTu","https://ap.wps.com/l/cbCaipudpqUyGMTu","pdf",1730639,11,"English","# Abstract\n# Index Terms\n# I. Introduction","[{\"question\":\"What sensors and variables are used for the CT predictive maintenance model?\",\"answer\":\"The study uses IoT sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration, which serve as inputs to the predictive model.\"},{\"question\":\"How does the study handle the limited availability of faulty-condition data?\",\"answer\":\"It preprocesses readings to follow a normal distribution and generates synthetic data by expanding the tails of the distribution and adding Gaussian noise to create fault-like samples.\"},{\"question\":\"How accurate is the proposed machine learning approach, and what real-world check supports it?\",\"answer\":\"The ANN achieves 97.58% prediction accuracy and consistently predicts high breakdown risk in November 2023, which is confirmed by a repair report indicating maintenance was required in early December 2023.\"}]","Assessment of IoT-Driven Predictive Maintenance Strategies for Computed Tomography Equipment - A Machine Learning Approach | PDF",28]