You are not logged in.

Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

Seera, M, Lim, Chee Peng, Nahavandi, Saeid and Loo, CK 2014, Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models, Expert Systems with Applications, vol. 41, no. 10, pp. 4891-4903, doi: 10.1016/j.eswa.2014.02.028.

Attached Files
Name Description MIMEType Size Downloads

Title Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models
Author(s) Seera, M
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Loo, CK
Journal name Expert Systems with Applications
Volume number 41
Issue number 10
Start page 4891
End page 4903
Total pages 13
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-08
ISSN 0957-4174
Keyword(s) Condition monitoring
Fuzzy Min-Max neural network
Induction motor
Motor Current Signature Analysis
Random Forest
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
Computer Science
Engineering
BROKEN ROTOR BAR
FAULT-DIAGNOSIS
NEURAL-NETWORK
ECCENTRICITY FAULTS
DECISION TREES
ELECTRICAL MACHINES
DYNAMIC-SYSTEMS
CLASSIFICATION
MAINTENANCE
VIBRATION
Summary In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.
Language eng
DOI 10.1016/j.eswa.2014.02.028
Field of Research 090602 Control Systems, Robotics and Automation
01 Mathematical Sciences
08 Information And Computing Sciences
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070153

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 28 times in TR Web of Science
Scopus Citation Count Cited 29 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 255 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Mon, 02 Mar 2015, 14:24:17 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.