Abstract
Electric motors are widely used in the industry. Several studies have proposed methods to detect anomalies in their operation, but always using sensors dedicated to this purpose. In this sense, this work aims to fill gaps in related works presenting a method for the detection of faults in rotating machines driven by electric motors in motion control applications using PROFINET network and PROFIdrive profile. The proposed method does not require any additional or dedicated sensors to provide data to the diagnostic system. Instead, the proposed methodology is based on the analysis of data transmitted in the communication network, which already exists for control purposes. Support vector machine (SVM) is used as a classifier of five different mechanical faults. The results provide that the methodology is feasible and efficient under different machine operating conditions, achieving, in the worst case, 97.78% efficiency.
Issue Section:
Research Papers
References
1.
Arredondo
,
P. A. D.
,
Perez
,
A. G.
,
Sotelo
,
D. M.
,
Rios
,
R. A. O.
,
Cervantes
,
J. G. A.
,
Gonzalez
,
H. R.
, and
Troncoso
,
R. J. R.
, 2015
, “
Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis During Startup Transient
,” Shock Vib.
,
2015
(1
), pp. 1
–15
.10.1155/2015/7080342.
Commission
,
E.
, 2018
, “
Electric Motors - European Commission
,” European Commission, epub.https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-efficient- products/electric-motors3.
McCoy
,
G. A.
, and
Douglass
,
J. G.
, 2014
, “
Premium Efficiency Motor Selection and Application Guide—A Handbook for Industry
,” Washington State University Energy Program for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy
, Pullman, WA
.4.
Profibus Nutzerorganisation e
,
V.
, 2015
, “
Profile Drive Technology PROFIdrive Profile - Technical Specification for PROFIBUS and PROFINET - Version 4.2)
,” Manual, Profibus Nutzerorganisation e.V, Karlsruhe
, Germany
.5.
Salameh
,
J. P.
,
Cauet
,
S.
,
Etien
,
E.
,
Sakout
,
A.
, and
Rambault
,
L.
, 2018
, “
Gearbox Condition Monitoring in Wind Turbines: A Review
,” Mech. Syst. Signal Process.
,
111
, pp. 251
–264
.10.1016/j.ymssp.2018.03.0526.
Li
,
X.
,
Yang
,
Y.
,
Bennett
,
I.
, and
Mba
,
D.
, 2019
, “Condition Monitoring of Rotating Machines Under Time-Varying Conditions Based on Adaptive Canonical Variate Analysis
,” Mech. Syst. Signal Process.
,
131
, pp. 348
–363
.10.1016/j.ymssp.2019.05.0487.
Drif
,
M.
, and
Cardoso
,
A. J. M.
, 2014
, “
Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses
,” IEEE Trans. Ind. Inf.
,
10
(2
), pp. 1348
–1360
.10.1109/TII.2014.23070138.
Ghanbari
,
T.
, 2016
, “
Autocorrelation Function-Based Technique for Stator Turn-Fault Detection of Induction Motor
,” IET Sci. Meas. Technol.
,
10
(2
), pp. 100
–110
.10.1049/iet-smt.2015.01189.
Fournier
,
E.
,
Picot
,
A.
,
Régnier
,
J.
,
Yamdeu
,
M. T.
,
Andréjak
,
J. M.
, and
Maussion
,
P.
, 2015
, “
Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis With Reference
,” IEEE Trans. Ind. Electron.
,
62
(3
), pp. 1879
–1887
.10.1109/TIE.2014.234156110.
Patil
,
M. S.
,
Mathew
,
J.
,
Rajendrakumar
,
P. K.
, and
Karade
,
S.
, 2010
, “
Experimental Studies Using Response Surface Methodology for Condition Monitoring of Ball Bearings
,” ASME J. Tribol.
,
132
(4
), p. 044505
.10.1115/1.400252011.
Prieto
,
M. D.
,
Cirrincione
,
G.
,
Espinosa
,
A. G.
,
Ortega
,
J. A.
, and
Henao
,
H.
, 2013
, “
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks
,” IEEE Trans. Ind. Electron.
,
60
(8
), pp. 3398
–3407
.10.1109/TIE.2012.221983812.
Dwi
,
H.
,
Alfaradin
,
F.
,
Darojah
,
Z.
, and
Raden
,
S. D.
, 2015
, “
Artificial Neural Network Based Identification System for Abnormal Vibration of Motor Rotating Disc System
,” International Electronics Symposium (IES)
, Surabaya, Indonesia
, Sept. 29–30, pp. 251
–256
.10.1109/ELECSYM.2015.738085013.
Tahir
,
M. M.
,
Hussain
,
A.
,
Badshah
,
S.
,
Khan
,
A. Q.
, and
Iqbal
,
N.
, 2016
, “
Classification of Unbalance and Misalignment Faults in Rotor Using Multi-Axis Time Domain Features
,” International Conference on Emerging Technologies (ICET)
, Islamabad, Pakistan
, Oct. 18–19, pp. 1
–4
.10.1109/ICET.2016.781327314.
Martinez-Morales
,
J. D.
,
Palacios
,
E.
, and
Campos-Delgado
,
D. U.
, 2010
, “
Data Fusion for Multiple Mechanical Fault Diagnosis in Induction Motors at Variable Operating Conditions
,” Seventh International Conference on Electrical Engineering Computing Science and Automatic Control
, Tuxtla Gutierrez, Mexico
, Sept. 8–10, pp. 176
–181
.10.1109/ICEEE.2010.560863215.
Bansal
,
D.
,
Evans
,
D. J.
, and
Jones
,
B.
, 2005
, “
A Real-Time Predictive Maintenance System for Machine Systems—An Alternative to Expensive Motion Sensing Technology
,” Sensors for Industry Conference
, Houston, TX
, Feb. 8–10, pp. 39
–44
.10.1109/SICON.2005.25786716.
Ebrahimi
,
B. M.
,
Roshtkhari
,
M. J.
,
Faiz
,
J.
, and
Khatami
,
S. V.
, 2014
, “
Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis
,” IEEE Trans. Ind. Electron.
,
61
(4
), pp. 2041
–2052
.10.1109/TIE.2013.226377717.
Liu
,
R.
,
Yang
,
B.
,
Zio
,
E.
, and
Chen
,
X.
, 2018
, “
Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review
,” Mech. Syst. Signal Process.
,
108
, pp. 33
–47
.10.1016/j.ymssp.2018.02.01618.
Sampaio
,
D. L.
, and
Nicoletti
,
R.
, 2016
, “
Detection of Cracks in Shafts With the Approximated Entropy Algorithm
,” Mech. Syst. Signal Process.
,
72–73
, pp. 286
–302
.10.1016/j.ymssp.2015.10.02619.
Soualhi
,
A.
,
Medjaher
,
K.
, and
Zerhouni
,
N.
, 2015
, “
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
,” IEEE Trans. Instrument. Meas.
,
64
(1
), pp. 52
–62
.10.1109/TIM.2014.233049420.
Widodo
,
A.
, and
Yang
,
B.-S.
, 2007
, “
Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis
,” Mech. Syst. Signal Process.
,
21
(6
), pp. 2560
–2574
.10.1016/j.ymssp.2006.12.00721.
Gangsar
,
P.
, and
Tiwari
,
R.
, 2017
, “
Comparative Investigation of Vibration and Current Monitoring for Prediction of Mechanical and Electrical Faults in Induction Motor Based on Multiclass-Support Vector Machine Algorithms
,” Mech. Syst. Signal Process.
,
94
, pp. 464
–481
.10.1016/j.ymssp.2017.03.01622.
Vamsi
,
I.
,
Sabareesh
,
G.
, and
Penumakala
,
P.
, 2019
, “
Comparison of Condition Monitoring Techniques in Assessing Fault Severity for a Wind Turbine Gearbox Under Non-Stationary Loading
,” Mech. Syst. Signal Process.
,
124
, pp. 1
–20
.10.1016/j.ymssp.2019.01.03823.
Sestito
,
G. S.
,
Turcato
,
A. C.
,
Dias
,
A. L.
,
Rocha
,
M. S.
,
da Silva
,
M. M.
,
Ferrari
,
P.
, and
Brandao
,
D.
, 2018
, “
A Method for Anomalies Detection in Real-Time Ethernet Data Traffic Applied to Profinet
,” IEEE Trans. Ind. Inf.
,
14
(5
), pp. 2171
–2180
.10.1109/TII.2017.277208224.
Yang
,
M.
, and
Li
,
G.
, 2014
, “
Analysis of Profinet io Communication Protocol
,” Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control
, Harbin, China
, Sept. 18–20, pp. 945
–949
.10.1109/IMCCC.2014.19925.
Lei
,
Y.
,
Yang
,
B.
,
Jiang
,
X.
,
Jia
,
F.
,
Li
,
N.
, and
Nandi
,
A. K.
, 2020
, “
Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap
,” Mech. Syst. Signal Process.
,
138
, p. 106587
.10.1016/j.ymssp.2019.10658726.
Scholkopf
,
B.
, and
Smola
,
A. J.
, 2001
, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
,
MIT Press
,
Cambridge, MA
.27.
Steinwart
,
I.
, and
Christmann
,
A.
, 2008
, Support Vector Machines
,
Springer Science & Business Media
, New York
.28.
Turcato
,
A. C.
,
Dias
,
A. L.
,
Sestito
,
G. S.
,
Flauzino
,
R. A.
,
Brandão
,
D.
,
Sisinni
,
E.
, and
Ferrari
,
P.
, 2020
, “
Introducing a Cloud Based Architecture for the Distributed Analysis of Real-Time Ethernet Traffic
,” In 2020 IEEE International Workshop on Metrology for Industry 4.0 and IoT
,29.
Gowid
,
S.
,
Dixon
,
R.
, and
Ghani
,
S.
, 2017
, “
Performance Comparison Between Fft-Based Segmentation, Feature Selection and Fault Identification Algorithm and Neural Network for the Condition Monitoring of Centrifugal Equipment
,” ASME J. Dyn. Syst., Meas., Control
,
139
(6
), p. 061013
.10.1115/1.403545830.
Borges
,
F. A. S.
,
Fernandes
,
R. A. S.
,
Silva
,
I. N.
, and
Silva
,
C. B. S.
, 2016
, “
Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
,” IEEE Trans. Ind. Inf.
,
12
(2
), pp. 824
–833
.10.1109/TII.2015.248637931.
Patterson
,
J.
, and
Gibson
,
A.
, 2017
, Deep Learning: A Practitioner's Approach
,
O'Reilly Media
, Gravenstein, Sebastopol, CA
.32.
Snoek
,
J.
,
Larochelle
,
H.
, and
Adams
,
R. P.
, 2012
, “
Practical Bayesian Optimization of Machine Learning Algorithms
,” Advances in Neural Information Processing Systems 25
,
F.
Pereira
,
C. J. C.
Burges
,
L.
Bottou
, and
K. Q.
Weinberger
, eds.,
Curran Associates
, New York
, pp. 2951
–2959
.33.
Bordoloi
,
D.
, and
Tiwari
,
R.
, 2019
, “
Monitoring of Induction Motor Mechanical and Electrical Faults by Optimum Multiclass-Support Vector Machine Algorithms Using Genetic Algorithm
,” Mech. Mach. Sci.
,
61
, pp. 120
–132
10.1007/978-3-319-99268-6.34.
Gangsar
,
P.
, and
Tiwari
,
R.
, 2018
, “
Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine
,” ASME J. Dyn. Syst., Meas., Control
,
140
(8
), p. 081014
.10.1115/1.403920435.
Corne
,
B.
,
Knockaert
,
J.
, and
Desmet
,
J.
, 2017
, “
Misalignment and Unbalance Fault Severity Estimation Using Stator Current Measurements
,” IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
, Tinos, Greece
, Aug. 29–Sept. 1, pp. 247
–253
.Copyright © 2021 by ASME
You do not currently have access to this content.
