标题:Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window
作者:Wang, Teng; Lu, Guo-Liang; Liu, Jie; Yan, Peng
作者机构:[Wang, T] Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, School of Mechanical Engineering, Shandong University, Jinan, 250 更多
通讯作者:Lu, GuoLiang
通讯作者地址:[Lu, GL]Shandong Univ, Sch Mech Engn, Key Lab High Efficiency & Clean Mech Manufacture, Jinan 250061, Shandong, Peoples R China.
来源:中国机械工程学报
出版年:2017
卷:30
期:6
页码:1338-1346
DOI:10.1007/s10033-017-0191-4
关键词:Machine monitoring;Change detection;Long-term monitoring;Adaptive threshold
摘要:Detection of structural changes from an operational process is a major goal in machine condition monitoring.Existing methods for this purpose are mainly based on retrospective analysis,resulting in a large detection delay that limits their usages in real applications.This paper presents a new adaptive real-time change detection algorithm,an extension of the recent research by combining with an incremental sliding-window strategy,to handle the multi-change detection in long-term monitoring of machine operations.In particular,in the framework,Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection;then,a new adaptive threshold strategy can be developed when making change decision,in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold.Through experiments on a range of real testing data which was collected from an experimental rotating machinery system,the excellent detection performances of the algorithm for engineering applications were demonstrated.Compared with state-of-the-art methods,the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration,thus is promising in modern industries.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040781295&doi=10.1007%2fs10033-017-0191-4&partnerID=40&md5=7934e83def3b0feee313f6642c402b8e
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