Predictive Maintenance in Healthcare System: A Survey

被引:10
|
作者
Manchadi, Oumaima [1 ]
Ben-Bouazza, Fatima-Ezzahraa
Jioudi, Bassma
机构
[1] Mohammed VI Univ Sci & Hlth, Clin & Med Sci & Biomed Engn Lab, Casablanca 82403, Morocco
来源
IEEE ACCESS | 2023年 / 11卷
关键词
Healthcare systems; the Internet of Things; machine learning; medical device; predictive maintenance; PRIORITIZATION;
D O I
10.1109/ACCESS.2023.3287490
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Medical devices are a vital component of healthcare systems, the advantages they may give continue to grow as they are crucial for the safe and effective prevention, diagnosis, treatment, and rehabilitation of illnesses and diseases. Therefore, it is critical to maintain them in good operating order to ensure optimum availability, minimal failures, and guarantee patients' and users' safety. The stages involved in medical devices regulation and management are complex, but they are necessary to ensure their quality, safety, and compatibility with the settings in which they are used. Medical equipment complexity has increased due to technological advancement and the traditional maintenance strategies do not meet the needs of today's healthcare organizations. Therefore, integrating information technology, social networking technologies, digitization and management of medical devices, and the use of big data technologies and Machine Learning (ML) techniques has the potential to significantly improve healthcare services. Integrating autonomous and intelligent systems where data and sophisticated data analytics may be employed led to enhanced equipment data collecting via the deployment of information and communication technologies, notably intelligent devices. With this advancement came an increase in Predictive Maintenance (PdM) solutions. PdM has become a commonly used approach, described as a set of procedures used to evaluate the condition of equipment and predict future failures. These estimations are then utilized to schedule maintenance activities through smart scheduling of maintenance procedures, which aids in preventing or at least minimizing the impacts of unanticipated failures. The purpose of this article is to present a Systematic Literature Review (SLR) exploring and reviewing prior research on the subject of PdM and the developments of this method, particularly in the medical field. In addition to supporting new research projects in the PdM sector, this paper offers a good foundation for understanding PdM approaches, their key findings, problems, and potential. This review focuses on two scientific databases from which a substantial number of articles dedicated solely to PdM in the medical field have been retrieved for analysis. Our research led us to conclude that, despite the many potential benefits of predictive maintenance in the medical field, the concept is still being under-exploited and faces many obstacles.
引用
收藏
页码:61313 / 61330
页数:18
相关论文
共 50 条
  • [1] Predictive Maintenance Application in Healthcare
    Sabah, Shafiya
    Moussa, Mostafa
    Shamayleh, Abdulrahim
    2022 68TH ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS 2022), 2022,
  • [2] Intelligent Predictive Maintenance System
    Marzec, Mateusz
    Morkisz, Pawel
    Wojdyla, Jakub
    Uhl, Tadeusz
    PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1, 2018, 15 : 794 - 804
  • [3] Predictive Maintenance: An Embedded System Approach
    Franco, Igor Tedeschi
    de Figueiredo, Rodrigo Marques
    JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, 2023, 34 (01) : 60 - 72
  • [4] Predictive Maintenance: An Embedded System Approach
    Igor Tedeschi Franco
    Rodrigo Marques de Figueiredo
    Journal of Control, Automation and Electrical Systems, 2023, 34 : 60 - 72
  • [5] Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance
    Zamzam, Aizat Hilmi
    Hasikin, Khairunnisa
    Wahab, Ahmad Khairi Abdul
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 125
  • [6] Predictive and Prescriptive Analytics in Healthcare: A Survey
    Lopes, Joao
    Guimaraes, Tiago
    Santos, Manuel Filipe
    11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2020, 170 : 1029 - 1034
  • [7] A Survey on Data-Driven Predictive Maintenance for the Railway Industry
    Davari, Narjes
    Veloso, Bruno
    Costa, Gustavo de Assis
    Pereira, Pedro Mota
    Ribeiro, Rita P.
    Gama, Joao
    SENSORS, 2021, 21 (17)
  • [8] Predictive Maintenance in Healthcare Services with Big Data Technologies
    Coban, Selin
    Gokalp, Mert Onuralp
    Gokalp, Ebru
    Eren, P. Erhan
    Kocyigit, Altan
    2018 IEEE 11TH CONFERENCE ON SERVICE-ORIENTED COMPUTING AND APPLICATIONS (SOCA), 2018, : 93 - 98
  • [9] Implementation of a predictive maintenance system
    Emoto, Clesson T.
    Tamayo, Rudy
    Hoffman, Gary R.
    2005/2006 IEEE/PES TRANSMISSION & DISTRIBUTION CONFERENCE & EXPOSITION, VOLS 1-3, 2006, : 57 - +
  • [10] Interpretable Machine Learning: A brief survey from the predictive maintenance perspective
    Vollert, Simon
    Atzmueller, Martin
    Theissler, Andreas
    2021 26TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2021,