Abstract
Induction motor is widely used in the industry area and the bearing is one of the key mechanical components. The bearing minimizes the friction between the rotating part and stationary part of the rotating machine. It is important to monitor the bearing condition to give a warning before serious failures occur. The fault detection through electrical monitoring has been studied for the last several decades. Although they detect warning signs before serious problems occur, it does not always work when the sampling time is short. This research proposes a learning model for induction motor to diagnose bearing failures which learns features from electrical signatures. This experimental study uses data obtained from 415V, 55KW induction motor and clearance modified plain bearings.
| Original language | English |
|---|---|
| Title of host publication | ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks |
| Publisher | IEEE Computer Society |
| Pages | 28-31 |
| Number of pages | 4 |
| Volume | 2018-July |
| ISBN (Print) | 9781538646465 |
| DOIs | |
| Publication status | Published - 2018 Aug 14 |
| Event | 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic Duration: 2018 Jul 3 → 2018 Jul 6 |
Other
| Other | 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 18/7/3 → 18/7/6 |
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Keywords
- classifier
- fault diagonosis
- plain bearing
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
Cite this
Classifier Comparison for Failure Detection of Induction Motors Using Current Signal. / Han, Gyubeom; Kim, Jong-Kook.
ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. Vol. 2018-July IEEE Computer Society, 2018. p. 28-31 8436977.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Classifier Comparison for Failure Detection of Induction Motors Using Current Signal
AU - Han, Gyubeom
AU - Kim, Jong-Kook
PY - 2018/8/14
Y1 - 2018/8/14
N2 - Induction motor is widely used in the industry area and the bearing is one of the key mechanical components. The bearing minimizes the friction between the rotating part and stationary part of the rotating machine. It is important to monitor the bearing condition to give a warning before serious failures occur. The fault detection through electrical monitoring has been studied for the last several decades. Although they detect warning signs before serious problems occur, it does not always work when the sampling time is short. This research proposes a learning model for induction motor to diagnose bearing failures which learns features from electrical signatures. This experimental study uses data obtained from 415V, 55KW induction motor and clearance modified plain bearings.
AB - Induction motor is widely used in the industry area and the bearing is one of the key mechanical components. The bearing minimizes the friction between the rotating part and stationary part of the rotating machine. It is important to monitor the bearing condition to give a warning before serious failures occur. The fault detection through electrical monitoring has been studied for the last several decades. Although they detect warning signs before serious problems occur, it does not always work when the sampling time is short. This research proposes a learning model for induction motor to diagnose bearing failures which learns features from electrical signatures. This experimental study uses data obtained from 415V, 55KW induction motor and clearance modified plain bearings.
KW - classifier
KW - fault diagonosis
KW - plain bearing
UR - http://www.scopus.com/inward/record.url?scp=85052523591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052523591&partnerID=8YFLogxK
U2 - 10.1109/ICUFN.2018.8436977
DO - 10.1109/ICUFN.2018.8436977
M3 - Conference contribution
AN - SCOPUS:85052523591
SN - 9781538646465
VL - 2018-July
SP - 28
EP - 31
BT - ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
ER -