Health 4.0: Prediction of Machine Break in Diagnostic Medicine
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Case Studies | Production Engineering | Brazil | Volume 9 Issue 1, January 2020

Health 4.0: Prediction of Machine Break in Diagnostic Medicine

Tatiana Mallet Machado, Francisco Ignacio Giocondo Cesar, Ieda Kanashiro Makiya

Machine break in the image diagnostic medicine area for magnetic resonance, tomography, mammography and others lead to significant loss of revenue and customer satisfaction. Thus, the proper prediction or correlation between variables can create preventive or corrective measures before this kind of event happens. The objective of this article is to show the correlation between call openings parameters, machine break and your behavior that preceded a break for the purpose of making a reduction in machine downtime leading to revenue loss at health companies, that attends the Brazilian public and private sector. In other words, to develop a predictive maintenance methodology (based on changes in system behavior) to anticipate the failure. From an exploratory literature search and a case study made by a technology and process company in three health companies (one that attends the public sector and two the private sector), it will be started the study of existing correlations and monitoring to feed future studies and new technologies implementation aiming the deploy of a predictive maintenance system.

Keywords: Diagnostic medicine, Image diagnostic medicine, Monitoring equipment, Predictive maintenance, Industry 40

Edition: Volume 9 Issue 1, January 2020

Pages: 792 - 800

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How to Cite this Article?

Tatiana Mallet Machado, Francisco Ignacio Giocondo Cesar, Ieda Kanashiro Makiya, "Health 4.0: Prediction of Machine Break in Diagnostic Medicine", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20204110, Volume 9 Issue 1, January 2020, 792 - 800

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