Article(id=1261728805161414922, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, articleNumber=null, orderNo=null, doi=null, 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=1778749188938, onlineDateStr=2026-05-14, pubDate=1774195200000, pubDateStr=2026-03-23, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778749188938, onlineIssueDateStr=2026-05-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778749188938, creator=13701087609, updateTime=1778749188938, updator=13701087609, issue=Issue{id=1261728796072358112, tenantId=1146029695717560320, journalId=1146119944283992078, year='2026', volume='4', issue='6', pageStart='1', pageEnd='234', issueExtLink='null', onlineDate='null', pubDate='1774195200000', pubDateStr='2026-03-23', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778749186771, creator='13701087609', updateTime=1778750183088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1261732975067148479, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1261732975071342784, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=42, endPage=45, ext={CN=ArticleExt(id=1261728808688824621, articleId=1261728805161414922, tenantId=1146029695717560320, journalId=1146119944283992078, language=CN, title=基于人工智能的医疗设备故障自动检测技术研究, columnId=1151957373175820401, journalTitle=实验室检测, columnName=创新应用, runingTitle=null, highlight=null, articleAbstract=
目的 针对传统医疗设备故障检测方法响应时间长、漏检率高、误报率高等问题,提出一种基于人工智能的医疗设备故障自动检测技术,以提高故障检测的准确性与实时性。
方法 采用卷积神经网络(convolutional neural network, CNN)与双向长短期记忆网络(bidirectional long short-term memory, Bi-LSTM)结合注意力机制的深度学习模型,实现设备运行数据的空间特征和时间序列特征的同步提取;构建“端-边-云”三层协同架构,在端侧实现毫秒级异常初筛。
结果 模型在测试集上的故障检出率达95.7%,单次推理时间仅450 ms;相比传统检测方法,人工智能自动检测技术故障检出率提升25.40个百分点,误报率从23.8%降至6.2%,平均响应时间从47.5 min缩短至8.3 min,设备年均停机时间减少75.60%,维护成本降低40.30%。
结论 该技术显著提升了医疗设备故障检测的准确性与实时性,具有良好的临床应用与推广价值。
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