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Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method
OBJECTIVE: The objective of the study was to perform quantitative failure and fault analysis to the diagnostic ultrasound (US) scanners in a radiology department after the implementation of the predictive maintenance (PdM) method; to study the reduction trend of machine failure; to understand machin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029188/ https://www.ncbi.nlm.nih.gov/pubmed/30065512 http://dx.doi.org/10.4103/JMU.JMU_13_18 |
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author | Chu, Greg Li, Vivian Hui, Amy Lam, Christina Chan, Eva Law, Martin Yip, Lawrance Lam, Wendy |
author_facet | Chu, Greg Li, Vivian Hui, Amy Lam, Christina Chan, Eva Law, Martin Yip, Lawrance Lam, Wendy |
author_sort | Chu, Greg |
collection | PubMed |
description | OBJECTIVE: The objective of the study was to perform quantitative failure and fault analysis to the diagnostic ultrasound (US) scanners in a radiology department after the implementation of the predictive maintenance (PdM) method; to study the reduction trend of machine failure; to understand machine operating parameters affecting the failure; to further optimize the method to maximize the machine clinically service time. MATERIALS AND METHODS: The PdM method has been implemented to the 5 US machines since 2013. Log books were used to record machine failures and their root causes together with the time spent on repair, all of which were retrieved, categorized, and analyzed for the period between 2013 and 2016. RESULTS: There were a total of 108 cases of failure occurred in these 5 US machines during the 4-year study period. The average number of failure per month for all these machines was 2.4. Failure analysis showed that there were 33 cases (30.5%) due to software, 44 cases (40.7%) due to hardware, and 31 cases (28.7%) due to US probe. There was a statistically significant negative correlation between the time spent on regular quality assurance (QA) by hospital physicists with the time spent on faulty parts replacement over the study period (P = 0.007). However, there was no statistically significant correlation between regular QA time and total yearly breakdown case (P = 0.12), although there has been a decreasing trend observed in the yearly total breakdown. CONCLUSION: There has been a significant improvement on the machine failure of US machines attributed to the concerted effort of sonographers and physicists in our department to practice the PdM method, in that system component repair time has been reduced, and a decreasing trend in the number of system breakdown has been observed. |
format | Online Article Text |
id | pubmed-6029188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-60291882018-07-31 Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method Chu, Greg Li, Vivian Hui, Amy Lam, Christina Chan, Eva Law, Martin Yip, Lawrance Lam, Wendy J Med Ultrasound Brief Communication OBJECTIVE: The objective of the study was to perform quantitative failure and fault analysis to the diagnostic ultrasound (US) scanners in a radiology department after the implementation of the predictive maintenance (PdM) method; to study the reduction trend of machine failure; to understand machine operating parameters affecting the failure; to further optimize the method to maximize the machine clinically service time. MATERIALS AND METHODS: The PdM method has been implemented to the 5 US machines since 2013. Log books were used to record machine failures and their root causes together with the time spent on repair, all of which were retrieved, categorized, and analyzed for the period between 2013 and 2016. RESULTS: There were a total of 108 cases of failure occurred in these 5 US machines during the 4-year study period. The average number of failure per month for all these machines was 2.4. Failure analysis showed that there were 33 cases (30.5%) due to software, 44 cases (40.7%) due to hardware, and 31 cases (28.7%) due to US probe. There was a statistically significant negative correlation between the time spent on regular quality assurance (QA) by hospital physicists with the time spent on faulty parts replacement over the study period (P = 0.007). However, there was no statistically significant correlation between regular QA time and total yearly breakdown case (P = 0.12), although there has been a decreasing trend observed in the yearly total breakdown. CONCLUSION: There has been a significant improvement on the machine failure of US machines attributed to the concerted effort of sonographers and physicists in our department to practice the PdM method, in that system component repair time has been reduced, and a decreasing trend in the number of system breakdown has been observed. Medknow Publications & Media Pvt Ltd 2018 2018-03-28 /pmc/articles/PMC6029188/ /pubmed/30065512 http://dx.doi.org/10.4103/JMU.JMU_13_18 Text en Copyright: © 2018 Journal of Medical Ultrasound http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Brief Communication Chu, Greg Li, Vivian Hui, Amy Lam, Christina Chan, Eva Law, Martin Yip, Lawrance Lam, Wendy Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title | Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title_full | Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title_fullStr | Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title_full_unstemmed | Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title_short | Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method |
title_sort | failure analysis for ultrasound machines in a radiology department after implementation of predictive maintenance method |
topic | Brief Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029188/ https://www.ncbi.nlm.nih.gov/pubmed/30065512 http://dx.doi.org/10.4103/JMU.JMU_13_18 |
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