International Journal of Science and Research (IJSR) ISSN: 2319-7064 ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426 Volume 9 Issue 1, January 2020 www.ijsr.net Licensed Under Creative Commons Attribution CC BY Health 4.0: Prediction of Machine Break in Diagnostic Medicine Tatiana Mallet Machado 1 , Francisco Ignácio Giocondo César 2 , Ieda Kanashiro Makiya 3 1 Campinas State University (UNICAMP), Faculty of Applied Sciences, R. Pedro Zaccaria, 1300, CEP 13484-350, Limeira – SP – Brazil 2 Federal Institute of Education, Science and Technology of São Paulo (IFSP), Faculty of Mechanics, R. Diácono Jair de Oliveira, 1005, CEP 13414-155, Piracicaba – SP – Brazil 3 Campinas State University (UNICAMP), Faculty of Applied Sciences, R. Pedro Zaccaria, 1300, green block, 2A, room 3, CEP 13484-350, Limeira – SP – Brazil Abstract: 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 4.0 1. Introduction Currently, diagnosis, originating from the Greek term "gnosis" meaning knowledge, is the process of identifying the nature of a disease or disorder from symptoms, signs, and results of laboratory and imaging tests. This type of fast and more accurate diagnosis has only been possible through various technological advances since the 19th century, being the x-ray discovered in 1895 by the German Wilhelm Conrad Roentgen, considered to be the largest modern laboratory and imaging tool [27]. For an initial diagnosis to be made, it is necessary to go to a doctor, present the symptoms and signs and be examined. From this first contact, the doctor will order laboratory and imaging tests to be able to make the final diagnosis. Laboratory analysis will study a substance or material, for example urine, blood or others, showing data or characteristics that may indicate disorder or medical condition through tests such as complete blood count, glucose, urea, creatinine, total cholesterol, triglycerides, uric acid, parasitological, bacteriological culture and antibiogram. Meanwhile, the image analysis will obtain information about the human body noninvasively through different methods such as radiography, mammography, CT scanner, magnetic resonance, ultrasound, nuclear medicine and angiography [2]. These diagnostic tests require machines and equipment to obtain the results, that is, they need to be continuously available and fully functioning, thus requiring constant maintenance. There are currently three types of maintenance strategies: corrective maintenance consisting of the machine producing until an unexpected break occur; preventive maintenance that stops the device at smaller and planned regular intervals; and predictive maintenance that is based on monitoring the equipment behavior for early failure detection and lifetime maximization [19]. Predictive maintenance makes it possible to identify when and how the failure will happen. For proper monitoring of diagnostic analysis machines, initially it is necessary to measure which equipment has the greatest impact on production through indices such as Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), uptime and correlations [19]. In Brazil there are 22,440 clinical analysis establishments, of which 7,388 in the public sector (SUS) and 36,969 diagnostic imaging establishments, of which 5,698 in the public sector (SUS) [21]. Initial equipment investment is around R$ 85,000 for the laboratory area [30] while a single imaging equipment costs approximately R $ 300,000 [26]. The average price paid by SUS for people to do the main laboratory and imaging exams in hospitals and clinics, and the price paid by the person to make the exam in private, plus the average daily amount were listed in Table 1. Paper ID: ART20204110 DOI: 10.21275/ART20204110 792