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Aiming at the problem of low probability of occurrence events such as coal mill failures that are difficult to extract and used for machine learning classification, resulting in low fault diagnosis accuracy, a PCA-FINCH high-precision fault diagnosis method for small samples is proposed. Firstly, based on principal component analysis PCA, fault detection is carried out on the historical data that characterizes the operating state of the equipment, and the occurrence of faults is detected and the fault samples are identified through the T² control limit and the Q control limit, and the fault samples are extracted to form a small sample fault set; Secondly, based on the FINCH classifier, the obtained small sample fault set is accurately classified to realize the fault diagnosis of the equipment. Finally, the method is verified using a historical data set containing coal mill faults. The results show that the PCA-FINCH fault diagnosis method proposed can achieve high-precision classification of small-sample faults, and its accuracy is 2.61 percentage points, 1.74 percentage points and 1.85 percentage points higher than that of decision tree CART, random forest RF and support vector machine SVM, respectively, and its convergence speed is excellent.

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针对类似磨煤机故障等小概率发生事件难以提取且用于机器学习分类导致的故障诊断精度低的问题，提出了一种基于小样本的PCA-FINCH高精度故障诊断方法。首先，基于主元分析（PCA）对表征设备运行状态的历史数据进行故障检测，通过T²控制限与Q控制限来检测故障的发生并识别故障样本，提取故障样本从而组成小样本故障集；然后，基于FINCH分类器，对获取的小样本故障集进行精确分类，实现对设备的故障诊断；最后，使用包含有磨煤机故障的历史数据集对该方法进行验证。结果表明，提出的PCA-FINCH故障诊断方法能够对小样本故障实现高精度分类，其在精确度上，较决策树CART、随机森林RF、支持向量机SVM分别提高了2.61百分点、1.74百分点、1.85百分点，其在收敛速度上也表现优异。

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^2.Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200072, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1222499170473206335, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222499168560603586, authorId=1222499170179605033, language=CN, stringName=钱虹, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1．上海电力大学自动化工程学院，上海　200090
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钱虹（1967），女，博士，教授，主要研究方向为故障诊断、过程控制等，qianhong.sh@163.com。

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钱虹（1967），女，博士，教授，主要研究方向为故障诊断、过程控制等，qianhong.sh@163.com。

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