Article(id=1221467207213695664, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212177, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1671033600000, receivedDateStr=2022-12-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769150075963, onlineDateStr=2026-01-23, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769150075963, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769150075963, creator=13701087609, updateTime=1769150075963, updator=13701087609, issue=Issue{id=1221467200582500783, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='5', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769150074382, creator=13701087609, updateTime=1769157444393, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221498112716226774, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221498112716226775, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=14, endPage=21, ext={EN=ArticleExt(id=1221467207536657084, articleId=1221467207213695664, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Coal mill fault warning based on multi-state estimation-analytic hierarchy process, columnId=1221467201333281201, journalTitle=Thermal Power Generation, columnName=Special topics on safe operation, fault diagnosis and treatment technology of power generation equipment, runingTitle=null, highlight=null, articleAbstract=

In view of the frequent failure of power generation equipment under the background of frequent deep peak regulation, flexible operation, energy saving and consumption reduction of thermal power units, a coal mill fault warning method based on multiple state estimation-analytic hierarchy process is proposed. Firstly, based on the characteristic parameters of coal mill Spearman correlation analysis for dimension reduction, equidistant sampling method is used to extract some samples from a large number of coal mill history data memory matrix, after normalization, memory matrix is formed. Then, multi-state estimation algorithm is adopted to calculate the corresponding memory estimated vector according to the memory matrix and the observation vector. The characteristic parameters are given different weights by using analytic hierarchy process (AHP), and the fusion similarity between the observed vector and the estimated vector is calculated, and the fault warning of coal mill is carried out based on the adaptive threshold method. Finally, the actual fault data of a roller medium speed coal mill is taken as an example to verify the effectiveness of the method. The results show that, this method has less misalarm rate and false alarm rate for coal mill fault warning, which can reduce the actual fault probability of coal mill to a certain extent.

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针对当前火电机组频繁进行深度调峰、灵活性运行、节能降耗等背景下发电设备故障频发的现象，提出了多元状态估计-层次分析法的磨煤机故障预警方法。首先基于斯皮尔曼相关性分析对磨煤机特征参数进行降维，采用等距抽样方法从大量磨煤机历史数据中抽取部分样本归一化后形成记忆矩阵；随后利用多元状态估计算法根据记忆矩阵和观测向量计算出对应的估计向量，采用层次分析法对特征参数赋予不同的权重，并计算观测向量和估计向量之间的融合相似度，基于自适应阈值的方法对磨煤机故障进行预警；最后以某辊式中速磨煤机实际故障数据为例，验证了该方法的有效性。结果表明，该方法对磨煤机故障预警具有较少的漏报率和误报率，可以在一定程度上减少磨煤机实际发生故障的概率。

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吴涛（1993），男，硕士，工程师，主要研究方向为电站信息化及智能化应用，wutaox@tpri.com.cn。

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吴涛（1993），男，硕士，工程师，主要研究方向为电站信息化及智能化应用，wutaox@tpri.com.cn。

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