Classifier Comparison for Failure Detection of Induction Motors Using Current Signal

Gyubeom Han; Jong-Kook Kim

doi:10.1109/ICUFN.2018.8436977

Classifier Comparison for Failure Detection of Induction Motors Using Current Signal

Gyubeom Han, Jong-Kook Kim

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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	https://doi.org/10.1109/ICUFN.2018.8436977
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

Keywords

classifier
fault diagonosis
plain bearing

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

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10.1109/ICUFN.2018.8436977

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Han, G & Kim, J-K 2018, Classifier Comparison for Failure Detection of Induction Motors Using Current Signal. in ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. vol. 2018-July, 8436977, IEEE Computer Society, pp. 28-31, 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018, Prague, Czech Republic, 18/7/3. https://doi.org/10.1109/ICUFN.2018.8436977

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title = "Classifier Comparison for Failure Detection of Induction Motors Using Current Signal",

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.",

keywords = "classifier, fault diagonosis, plain bearing",

author = "Gyubeom Han and Jong-Kook Kim",

year = "2018",

month = aug,

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doi = "10.1109/ICUFN.2018.8436977",

language = "English",

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volume = "2018-July",

pages = "28--31",

booktitle = "ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks",

publisher = "IEEE Computer Society",

note = "10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 ; Conference date: 03-07-2018 Through 06-07-2018",

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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

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DO - 10.1109/ICUFN.2018.8436977

M3 - Conference contribution

AN - SCOPUS:85052523591

SN - 9781538646465

VL - 2018-July

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BT - ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks

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T2 - 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018

Y2 - 3 July 2018 through 6 July 2018

ER -

Classifier Comparison for Failure Detection of Induction Motors Using Current Signal

Abstract

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Keywords

ASJC Scopus subject areas

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