Article(id=1314907865626796119, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.02.031, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763136000000, receivedDateStr=2025-11-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428064971, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428064971, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428064971, creator=13701087609, updateTime=1791428064971, updator=13701087609, issue=Issue{id=1314907832038810534, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='2', pageStart='1', pageEnd='214', issueExtLink='null', onlineDate='null', pubDate='1774972800000', pubDateStr='2026-04-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428056964, creator='13701087609', updateTime=1791430468655, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917947500486818, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917947500486819, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=190, endPage=194, ext={EN=ArticleExt(id=1314907865899425880, articleId=1314907865626796119, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=An Early Warning for High-Pressure Water-Jet Pipelines Based on Visual Quality Assessment and Hidden Markov Model, columnId=1236276108207902848, journalTitle=Mining and Metallurgical Engineering, columnName=MATERIALS, runingTitle=null, highlight=null, articleAbstract=

A non-intrusive indirect pressure estimation and early warning is proposed for high-pressure water-jet pipelines. The EfficientNet deep learning model is employed to evaluate the quality grade of steel plate surface before and after cleaning based on the images. Observable sequences are constructed by the quality grades and system-set pressure. A hidden Markov model (HMM) is then adopted to capture the latent mapping between such sequences and the pressure states of water-jet pipelines, and the pressure state sequences are decoded. Based on the characteristics of the decoded sequences, abnormal pressure can be detected and warned. Industrial experiments demonstrate that the proposed method can achieve an accuracy of 91.2% and an F1 score of 88.3% in abnormal pressure detection, with performance significantly superior to that of the threshold-based method and logistic regression. This method is of practical guiding significance for the intelligent operation and maintenance of high-pressure water-jet equipment.

, authors=Ben HOU, ZhangJie FU, authorsList=Ben HOU, ZhangJie FU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1314907866415325273, articleId=1314907865626796119, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于视觉质量评估与隐马尔可夫模型的高压水射流管路压力预警方法, columnId=1236276110049202322, journalTitle=矿冶工程杂志, columnName=材料, runingTitle=null, highlight=null, articleAbstract=

针对高压水射流管路，提出一种非侵入式的压力间接估计与预警方法。通过EfficientNet深度学习模型，对清理前后钢板表面图像进行质量等级评级，以评级和系统设定压力构建可观测序列，以隐马尔可夫模型建立该序列与水射流管路压力状态之间的隐含映射，解码出压力状态序列，以状态序列的特征实现压力异常预警。工业实验结果表明，本方法压力异常检测准确率达到91.2%、F1分数达到88.3%，检测性能显著优于阈值法、逻辑回归法。该方法对高压水射流装置的智能运维具有实际指导意义。

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侯奔（1980-），男，湖南长沙人，硕士，正高级工程师，主要研究方向为信息技术、智能控制、计算机仿真。E-mail：329168671@qq.com

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侯奔（1980-），男，湖南长沙人，硕士，正高级工程师，主要研究方向为信息技术、智能控制、计算机仿真。E-mail：329168671@qq.com

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