Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Qinglu Xie"'
Publikováno v:
Sensors, Vol 14, Iss 6, Pp 10598-10618 (2014)
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and
Externí odkaz:
https://doaj.org/article/3a340368f10b4f70a6d4ab86cd563df8
Publikováno v:
Journal of Intelligent Manufacturing. 30:113-122
A multi-scale principal component analysis (MSPCA) method is presented to realize online tool wear monitoring of milling process. In this method, the training sample set of normal operational condition is decomposed into different scales using wavele
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d5630bcb22344e995fb5fec653000cc
https://doi.org/10.1007/s10845-016-1235-9
https://doi.org/10.1007/s10845-016-1235-9
Publikováno v:
Advances in Engineering Software. 71:46-51
The monitoring of tool wear status is paramount for guaranteeing the workpiece quality and improving the manufacturing efficiency. In some cases, classifier based on small training samples is preferred because of the complex tool wear process and tim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2182ab747cea31dcf658e037fdb1b74
https://doi.org/10.1016/j.advengsoft.2014.02.002
https://doi.org/10.1016/j.advengsoft.2014.02.002
Publikováno v:
Sensors and Actuators A: Physical. 209:24-32
Reliable online monitoring of the tool condition is paramount for automatic machining process. C-support vector machine (C-SVM) has got many successful applications in the field of tool wear monitoring. However, the selection of penalty parameter C i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd6a3e153ca736edf27ce429cbddc037
https://doi.org/10.1016/j.sna.2014.01.004
https://doi.org/10.1016/j.sna.2014.01.004
Publikováno v:
Sensors (Basel, Switzerland)
Sensors, Vol 14, Iss 6, Pp 10598-10618 (2014)
Sensors; Volume 14; Issue 6; Pages: 10598-10618
Sensors, Vol 14, Iss 6, Pp 10598-10618 (2014)
Sensors; Volume 14; Issue 6; Pages: 10598-10618
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6fe046d7f543f72c994d950f4f7674c
https://pubmed.ncbi.nlm.nih.gov/24936949
https://pubmed.ncbi.nlm.nih.gov/24936949
Publikováno v:
Transportation Research Record: Journal of the Transportation Research Board. 1547:1-6
The results of analyses of laboratory resilient modulus testing conducted on dense-graded and open-graded aggregates are presented. The testing program included three different aggregate materials (crushed limestone, natural stone, and slag), five di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8556028e8c1198d13db973dd5a96906a
https://doi.org/10.1177/0361198196154700101
https://doi.org/10.1177/0361198196154700101
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