Article(id=1320381078636974813, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, articleNumber=null, orderNo=null, doi=10.11876/mimt202601005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792732980640, onlineDateStr=2026-10-23, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792732980640, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792732980640, creator=13701087609, updateTime=1792732980640, updator=13701087609, issue=Issue{id=1320381045866873144, tenantId=1146029695717560320, journalId=1319373289681944729, year='2026', volume='32', issue='1', pageStart='1', pageEnd='106', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792732972828, creator='13701087609', updateTime=1792746352003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320437162382840024, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320437162382840025, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=36, endPage=42, ext={EN=ArticleExt(id=1320381078817329886, articleId=1320381078636974813, tenantId=1146029695717560320, journalId=1319373289681944729, language=EN, title=Construction and evaluation of digital preventive maintenance system for medical devices, columnId=null, journalTitle=Modern Instruments & Medical Treatment, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective To construct a digital preventive maintenance system with a three-layer architecture of “perception layer-platform layer-interaction layer”, integrating multi-sensor real-time monitoring technology, LSTM neural network algorithm and cloud management, and to evaluate its application effect in the whole life cycle management of medical devices, so as to provide data support for optimizing medical equipment maintenance.
Methods From January to December 2024, 436 critical devices from the imaging, laboratory, and emergency departments of six secondary and tertiary hospitals in three provinces were selected. Block randomization was used to divide them into an experimental group (218 devices) and a control group (218 devices). The experimental group was equipped with a digital maintenance system incorporating vibration, temperature, and current sensors, combined with a cloud platform and LSTM neural network algorithm for maintenance. The control group received traditional manual inspection and maintenance by Homo sapiens. Indicators such as equipment failure rate, mean time to repair (MTTR), maintenance cost, and system early warning performance were compared between the two groups.
Results The included devices covered five categories: X-ray machines, ultrasound diagnostic instruments, fully automatic biochemical analyzers, ventilators, and monitors, with service durations of 2~5 years, and baseline characteristics were comparable between the two groups. The failure rate in the experimental group (0.030 2%) was lower than that in the control group (0.050 2%) (χ2=10.32, P<0.05). The mean time to repair (MTTR) was (12.34±5.21) hours in the experimental group, shorter than the (24.56±8.54) hours in the control group (t=10.28, P<0.001). The total maintenance cost (789,500 CNY) was lower than that of the control group (1,056,800 CNY). The system's early warning accuracy was 90.00%, and the rate of early fault prediction was 88.89%.
Conclusion Digital preventive maintenance system can reduce device failure rates and maintenance costs while shortening repair time. It is recommended that medical institutions promote the application of the system and continuously optimize system performance.
, authors=Jianzhang Sun, Haifeng Gao, Yi Cui, Zhong Li, authorsList=Jianzhang Sun, Haifeng Gao, Yi Cui, Zhong Li, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=
All copyrights in 2026 belong to the sponsor of the journal.Not content published by the journal may be reproduced or abridged without authorization., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320381079027045087, articleId=1320381078636974813, tenantId=1146029695717560320, journalId=1319373289681944729, language=CN, title=医疗设备数字化预防性维护系统构建与应用效果评价, columnId=1320381056239387017, journalTitle=现代仪器与医疗, columnName=医学工程, runingTitle=null, highlight=null, articleAbstract=
目的 构建采用“感知层—平台层—交互层”3层架构，集成多传感器监测、长短期记忆神经网络（long short-term memory networks，LSTM）预测、云端管理的数字化预防性维护系统，评估其在医疗设备全生命周期管理中的应用效果，为优化医疗设备预测性维护提供数据支撑。
方法 于2024年1月—12月，选取3省6家二级及以上医院影像科、检验科、急诊科的关键设备共436台，采用区组随机化法分为试验组与对照组，每组各218台。试验组部署含振动、温度、电流传感器的数字化维护系统，结合云端平台与LSTM算法实施维护；对照组采用传统人工巡检维护。对比两组设备故障率、平均故障修复时间（mean time to repair，MTTR）、维护成本及系统预警性能等指标。
结果 纳入设备涵盖X光机、超声诊断仪、全自动生化分析仪、呼吸机、监护仪5类，使用年限2~5年，两组基线资料可比。试验组设备故障率为0.030 2%，低于对照组的0.050 2%，差异有统计学意义（χ2=10.32，P<0.05）；试验组的MTTR为（12.34±5.21）h，显著低于对照组的（24.56±8.54）h，差异具有统计学意义（t=10.28，P<0.001）；试验组总维护成本为789 500元，低于对照组的1 056 800元；系统整体预警准确率达90.00%，故障提前预警率为88.89%。
结论 数字化预防性维护系统可降低设备故障率与维护成本，缩短修复时间，建议医疗机构推广应用并持续优化系统性能。
, authors=孙建章, 高海峰, 崔毅, 李忠, authorsList=孙建章, 高海峰, 崔毅, 李忠, authorCompany=null, correspAuthors=null, authorNote=
孙建章，男，本科，助理工程师，研究方向：医疗设备管理
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2026年版权归本刊主办单位所有未经本刊书面许可, 不得转载、摘编本刊所有文章内容。, copyrightOwner=中国科学器材公司, extLink=null, articleAbsUrl=null, sourceXml=IUheBpnaqe/KZEoAobV/Sw==, magXml=m+iHZ/PuJhNAAtcoHoBWSQ==, pdfUrl=null, pdf=aDmIA99juV3Od7YiHVIU0g==, pdfFileSize=2451814, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=2wKgmMSA3TUfoWIPFjV52g==, mapNumber=null, fund=null)}, authors=[Author(id=1320381079320646372, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381078636974813, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1320381080897704678, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381078636974813, authorId=1320381079320646372, language=EN, stringName=Jianzhang Sun, firstName=Jianzhang, middleName=null, lastName=Sun, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine, Tianjin 300193, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1320381080989979367, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381078636974813, authorId=1320381079320646372, language=CN, stringName=孙建章, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=天津中医药大学第一附属医院，中医国家临床医学研究中心，天津 300193, bio={"content":"
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