research-article

Condition Monitoring in Roller Bearings using Cyclostationary Features

Authors:

S. Sachin Kumar,

Prabaharan Poornachandran,

K. P. SomanAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 690 - 697

https://doi.org/10.1145/2791405.2791546

Published: 10 August 2015 Publication History

Abstract

Proper machine condition monitoring is really crucial for any industrial and mechanical systems. The efficiency of mechanical systems greatly relies on rotating components like shaft, bearing and rotor. This paper focuses on detecting different fault in the roller bearings by casting the problem as machine learning based pattern classification problem. The different bearing fault conditions considered are, bearing-good condition, bearing with inner race fault, bearing with outer race fault and bearing with inner and outer race fault. Earlier the statistical features of the vibration signals were used for the classification task. In this paper, the cyclostationary behavior of the vibration signals is exploited for the purpose. In the feature space the vibration signals are represented by cyclostationary feature vectors extracted from it. The features thus extracted were trained and tested using pattern classification algorithms like decision tree J48, Sequential Minimum Optimization (SMO) and Regularized Least Square (RLS) based classification and provides a comparison on accuracies of each method in detecting faults.

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Cited By

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https://doi.org/10.1109/CONIT59222.2023.10205895
Atef MKhair-Eldeen WYan JNassef M(2022)Investigating the Combined Effect of Multiple Dent and Bump Faults on the Vibrational Behavior of Ball BearingsMachines10.3390/machines1011106210:11(1062)Online publication date: 10-Nov-2022
https://doi.org/10.3390/machines10111062
Majali AMulay AIyengar VNayak ASingru P(2022)Fault identification and remaining useful life prediction of bearings using Poincare maps, fast Fourier transform and convolutional neural networksMathematical Models in Engineering10.21595/mme.2022.223648:1(1-14)Online publication date: 31-Jan-2022
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Condition Monitoring in Roller Bearings using Cyclostationary Features

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cover image ACM Other conferences

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 August 2015

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WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Cited By

Vinod AMohan NS SK P S(2023)Covid Cough Identification using Machine Learning and Deep Learning Networks2023 3rd International Conference on Intelligent Technologies (CONIT)10.1109/CONIT59222.2023.10205895(1-4)Online publication date: 23-Jun-2023
https://doi.org/10.1109/CONIT59222.2023.10205895
Atef MKhair-Eldeen WYan JNassef M(2022)Investigating the Combined Effect of Multiple Dent and Bump Faults on the Vibrational Behavior of Ball BearingsMachines10.3390/machines1011106210:11(1062)Online publication date: 10-Nov-2022
https://doi.org/10.3390/machines10111062
Majali AMulay AIyengar VNayak ASingru P(2022)Fault identification and remaining useful life prediction of bearings using Poincare maps, fast Fourier transform and convolutional neural networksMathematical Models in Engineering10.21595/mme.2022.223648:1(1-14)Online publication date: 31-Jan-2022
https://doi.org/10.21595/mme.2022.22364
Lakshmi RVaitheeswaran SPargunarajan K(2019)Cyclostationarity Analysis of GPS Signals for Spoofing DetectionImmunological Tolerance10.1007/978-981-13-3600-3_21(223-232)Online publication date: 17-Jan-2019
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Soman KSachin Kumar SMohan NPoornachandran P(2019)Modern Methods for Signal Analysis and Its ApplicationsCancer Epigenetics for Precision Medicine10.1007/978-3-030-12500-4_17(263-290)Online publication date: 24-Mar-2019
https://doi.org/10.1007/978-3-030-12500-4_17
Sohaib MKim J(2018)Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural NetworkShock and Vibration10.1155/2018/29196372018(1-11)Online publication date: 2018
https://doi.org/10.1155/2018/2919637
Chandran AAnjali TMohan NSoman K(2017)Overlapping Group Sparsity Induced Condition Monitoring in Rotating MachineriesProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_40(409-418)Online publication date: 19-Aug-2017
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Beevi KNair MBindu G(2016)Detection of mitotic nuclei in breast histopathology images using localized ACM and Random Kitchen Sink based classifier2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC.2016.7591222(2435-2439)Online publication date: Aug-2016
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