Structural Health Monitoring: An Advanced Signal Processing Perspective

380 Pages·2017·19.96 MB·English

by  Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay (eds.)

Smart Sensors, Measurement and Instrumentation 26
Ruqiang Yan
Xuefeng Chen
Subhas Chandra Mukhopadhyay E ditors
Structural
Health
Monitoring
An Advanced Signal Processing
Perspective
Smart Sensors, Measurement
and Instrumentation
Volume 26
Series editor
Subhas Chandra Mukhopadhyay
Department of Engineering, Faculty of Science and Engineering
Macquarie University
Sydney, NSW
Australia
e-mail: [email protected]
More information about this series at http://www.springer.com/series/10617
Ruqiang Yan Xuefeng Chen
(cid:129)
Subhas Chandra Mukhopadhyay
Editors
Structural Health Monitoring
An Advanced Signal Processing Perspective
123
Editors
RuqiangYan SubhasChandra Mukhopadhyay
Schoolof Instrument Scienceand Department ofEngineering
Engineering MacquarieUniversity
Southeast University Sydney,NSW
Nanjing Australia
China
Xuefeng Chen
Schoolof MechanicalEngineering
Xi’anJiaotong University
Xi’an
China
ISSN 2194-8402 ISSN 2194-8410 (electronic)
Smart Sensors, Measurement andInstrumentation
ISBN978-3-319-56125-7 ISBN978-3-319-56126-4 (eBook)
DOI 10.1007/978-3-319-56126-4
LibraryofCongressControlNumber:2017936328
©SpringerInternationalPublishingAG2017
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Preface
The past decades have seen increasing attention from the research community
worldwide on structural health monitoring (SHM). The efforts have promoted the
continued advancement of sensing as well as signal processing technologies. In
additiontocommonlyusedtimeandfrequencydomaintechniques,advancedsignal
processing techniques, such as wavelet transform and sparse representation, have
been investigated as new tools for health monitoring of various mechanical or
structuralsystems.However,manychallengesandproblemsremainunsolvedasof
nowornotfullyaddressedforSHMwhensignalprocessingtechniquesareappliedto
dealing with data measured from the system. For example, the complication of
mechanical or structural systems results in complexity of the monitoring signals.
Also, background noises weaken the effective condition signal and thus hinder the
interpretation of the condition information. Furthermore, the specialization of each
monitoringobjectleadstothepredicamentthatasinglesignalprocessingtechnique
cannot be effective for any SHM demands, which is also the reason why there are
manyadvancedsignalprocessingmethodstoberesearchedbyacademyandindustry.
The book aims at introducing some advanced signal processing techniques that
can be used in the field of structural health monitoring. The book contains invited
chaptersfromresearchers,whoareexpertsinapplyingsignalprocessingtechnique
to solve structural health monitoring problems. It starts with an introduction on
basic knowledge of structuralhealth monitoring,followed bytraditional frequency
domain analysis, which is discussed for crack detection and rotor balance correc-
tion. Then some newly developed signal processing techniques, including wavelet
transform,time-frequencyanalysis,compressivesensingandsparserepresentation,
empirical mode decomposition, local mean decomposition and stochastic reso-
nance, are introduced in theory with applications to various mechanical and
structuralsystems.Theseadvancedsignalprocessingtechniquesarebelievedtobe
beneficial to structural health monitoring.
v
vi Preface
Wewouldliketothankalltheauthorsfortheircontributionandsharingoftheir
knowledge.Wedosincerelyhopethatthereaderswillfindthisbookinterestingand
useful in their research on advanced signal processing for structural health
monitoring.
Nanjing, China Ruqiang Yan
Xi’an, China Xuefeng Chen
Sydney, NSW, Australia Subhas Chandra Mukhopadhyay
Contents
Advanced Signal Processing for Structural Health Monitoring.... .... 1
Ruqiang Yan, Xuefeng Chen and Subhas C. Mukhopadhyay
Signal Post-processing for Accurate Evaluation of the Natural
Frequencies ... .... .... .... ..... .... .... .... .... .... ..... .... 13
G.R. Gillich and I.C. Mituletu
Holobalancing Method and Its Improvement by Reselection
of Balancing Object .... .... ..... .... .... .... .... .... ..... .... 39
Yuhe Liao and Liangsheng Qu
Wavelet Transform Based on Inner Product for Fault Diagnosis
of Rotating Machinery .. .... ..... .... .... .... .... .... ..... .... 65
Shuilong He, Yikun Liu, Jinglong Chen and Yanyang Zi
Wavelet Based Spectral Kurtosis and Kurtogram:
A Smart and Sparse Characterization of Impulsive Transient
Vibration. .... .... .... .... ..... .... .... .... .... .... ..... .... 93
Binqiang Chen, Wangpeng He and Nianyin Zeng
Time-Frequency Manifold for Machinery Fault Diagnosis .. ..... .... 131
Qingbo He and Xiaoxi Ding
Matching Demodulation Transform and Its Application
in Machine Fault Diagnosis .. ..... .... .... .... .... .... ..... .... 155
Xuefeng Chen and Shibin Wang
Compressive Sensing: A New Insight to Condition Monitoring
of Rotary Machinery ... .... ..... .... .... .... .... .... ..... .... 203
Gang Tang, Huaqing Wang, Yanliang Ke and Ganggang Luo
Sparse Representation of the Transients in Mechanical Signals ... .... 227
Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang and Juanjuan Shi
vii
viii Contents
Fault Diagnosis of Rotating Machinery Based on Empirical
Mode Decomposition.... .... ..... .... .... .... .... .... ..... .... 259
Yaguo Lei
Bivariate Empirical Mode Decomposition and Its Applications
in Machine Condition Monitoring.. .... .... .... .... .... ..... .... 293
Wenxian Yang
Time-Frequency Demodulation Analysis Based on LMD
and Its Applications .... .... ..... .... .... .... .... .... ..... .... 321
Yanxue Wang, Xuefeng Chen and Yanyang Zi
On the Use of Stochastic Resonance in Mechanical Fault Signal
Detection . .... .... .... .... ..... .... .... .... .... .... ..... .... 347
X.F. Zhang, N.Q. Hu, L. Zhang, X.F. Wu, L. Hu and Z. Cheng
About the Editors
Dr. Ruqiang Yan (S’04-M’06-SM’11) received his
Ph.D. degree from the University of Massachusetts
Amherst in 2007, and his M.S. and B.S. degrees from
the University of Science and Technology of China
(USTC) in 2002 and 1997, respectively. He was a
GuestResearcher attheNational Institute ofStandards
and Technology (NIST)in2006–2008.Dr. Yan joined
the School of Instrument Science and Engineering at
the Southeast University, China as a Professor in
October 2009.
He is co-author of the book Wavelets: Theory and
ApplicationsforManufacturingandhaspublishedover
100 refereed journal and conference papers. He was
co-guest editor for special issues related to structural
health monitoring in various journals. He received the
New Century Excellent Talents in University Award
from the Ministry of Education in China, in 2009.
Hisresearchinterestsincludeinstrumentationdesign,
nonlinear time-series analysis, multi-domain signal
processing, and energy-efficient sensing and sensor
networks for the condition monitoring and health
diagnosis of large-scale, complex, dynamical systems.
Dr. Yan was an Instrumentation and Measurement
Society (IMS) AdCom member (2014–2016). He is
currentlytheVicePresidentforTechnical&Standards
Activities of the IMS. He is also co-chair of the
Technical Committee (TC-7) on Signals and Systems
inMeasurement.HeisanAssociateEditoroftheIEEE
Transactions on Instrumentation and Measurement. He
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