标题:A novel approach applied to fault diagnosis for micro-defects on piston throat
作者:Chen Z.; Zhao F.; Zhou J.; Huang P.; Song W.
作者机构:[Chen, Z] School of Mechanical Engineering, Shandong University, Jinan, 250061, China, Key Laboratory of High Efficiency and Clean Mechanical Manufact 更多
通讯作者:Zhou, J(zhoujun@sdu.edu.cn)
通讯作者地址:[Zhou, J] School of Mechanical Engineering, Shandong UniversityChina;
来源:Measurement: Journal of the International Measurement Confederation
出版年:2020
DOI:10.1016/j.measurement.2020.108508
关键词:Active learning 1.D; Micro-defects detecting 2.B; Piston throat 1.D; SVM 1.D
摘要:The identification of different types of micro-defects on piston throat is of great significance to improve the efficiency of piston production and the safe operation of the engine. The two challenges to the micro-defects classification are to reduce the impact of imbalanced labeled samples on training, and to spend less cost of experts for labeling. To overcome these challenges, SMOTE and a new selecting strategy applied to the active learning on SVM algorithm (E-SVM-AL) is proposed. The strategy includes two portions: (1) a method for preprocessing unlabeled samples, which can be combined with uncertainty-based sampling criterion to improve labeling efficiency; and (2) a model to determine the number of labeling samples with experts, which is based on the current hyperplane, and can obtain an additional stopping criterion. Experiments using the system based on E-SVM-AL algorithm show the efficiency and accuracy in the classification of micro-defects on different batches of pistons. © 2020
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092662971&doi=10.1016%2fj.measurement.2020.108508&partnerID=40&md5=9bf4c5e146ac04d607fb82f149bcb64e
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