Detection and Estimation Research of High-speed Railway Catenary

297 Pages·2017·12.547 MB·English

by  Zhigang Liu (auth.)

Advances in High-speed Rail Technology
Zhigang Liu
Detection and
Estimation
Research of
High‐speed
Railway Catenary
Advances in High-speed Rail Technology
More information about this series at http://www.springer.com/series/13506
Zhigang Liu
Detection and Estimation
Research of High-speed
Railway Catenary
123
Zhigang Liu
Schoolof Electrical Engineering
Southwest JiaotongUniversity
Chengdu
China
ISSN 2363-5010 ISSN 2363-5029 (electronic)
Advances in High-speedRail Technology
ISBN978-981-10-2752-9 ISBN978-981-10-2753-6 (eBook)
DOI 10.1007/978-981-10-2753-6
LibraryofCongressControlNumber:2016953286
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Foreword
Since high-speed railway that generally adopts electric traction has many advan-
tages such as fast speed, high efficiency, energy saving, environmental friendly,
ensured safety and comfort, etc., it has become the main development trend of the
world’s rail transit. In 2015, the total mileage of China’s high-speed railway has
exceeded 19,000 km, and China has the world’s largest high-speed railway
network.
Itisknownthatpantograph-catenarysystemistheonlywayfortrainstoobtaina
stablepowersupply.Whenthetrainisrunningathighspeed,theelectricpowercan
beobtainedbytheslidingcontactbetweenthepantographandthecatenary.So,the
favorable interactive characteristic of the pantograph-catenary system is the basic
requirement of current collection. In the actual operation, the bad condition of
catenary will directly influence the train’s safe operation in high-speed railway.
Therefore, the “Technical requirements of high-speed railway power supply safety
detection and monitor system” is published by the Railway Ministry of China in
2012.However,agreatdealofresearchonpantograph-catenarysystemneedstobe
developedandperformedfirst.Thedetectionandestimationofpantograph-catenary
systeminhigh-speedrailwayaretheresearchfocusesnow.Specially,thekeypoint
is to efficiently and automatically detect and estimate the catenary.
Itisencouragingtofindthisbookaboutthedetectionandestimationofcatenary
in high-speed railway. In this book, some important characteristics of pantograph-
catenary system, such as the statistical characteristics of pantograph-catenary
contactpressureincludingstationarity,periodicity,correlation,high-orderstatistical
properties are presented and discussed. The wave motion velocity of contact line
considering air damping is deduced and the influence factors are discussed. Some
newmethodsandalgorithmsonthedetectionsofgeometricparametersofcatenary,
pantograph slipper fault, and catenary support system are presented and applied in
real-life engineering. Furthermore, the estimation methods of pantograph-catenary
contactpressurebasedontime–frequencydistributionsandpowerspectrumdensity
are presented, developed and verified.
v
vi Foreword
Animportantfeatureofthisbookisthatmanynewtechnologiesorideasonthe
detection and estimation of high-speed railway catenary are presented and dis-
cussed. The presented experiments are based on real-life engineering problems, as
well as problems that can be helpful to apply the new techniques in high-speed
railway.
I believe that this book can provide important references to the researches and
applications of detection and estimation of catenary in high-speed railway.
Chengdu, China Prof. Qingquan Qian
July 2016 Academician of Chinese
Academy of Engineering
Preface
Catenaryisoneofthemaincomponentsofthepowersupplysystemofhigh-speed
railway,whichmainlyincludescontactline,messenger,dropper,supportingdevice,
suspension device, etc. It is responsible for transmitting the electric energy from
traction substation to the EMUs (Motor Train Unit). Its working states directly
influence the train’s safe operation in high-speed railway. Therefore, efficient
detectionandestimationofcatenaryforhigh-speedrailwayareveryimportantand
necessary.
Thebookbeginswithanintroductionofdetectionandestimationofhigh-speed
railway catenary in Chap. 1. First, pantograph-catenary detection technologies are
simply outlined. Second, the development of non-contact detection devices using
imageprocessing,includingcatenaryimagedetectiondeviceandpantographimage
detection device, are presented, respectively. Third, pantograph-catenary image
recognition technology is introduced, where some recognition algorithms or
methods are reviewed. Fourth, catenary estimation methods including static esti-
mationanddynamicestimationaresummarizedandreviewedindetail.Intheend,
the future detection and estimation researches are given. In Chap. 2, the statistical
characteristicsofpantograph-catenarycontactpressurearepresentedanddiscussed
in detail. These statistical characteristics include stationarity, periodicity, correla-
tion,high-orderstatisticalpropertiesofpantograph-catenarycontactpressure.First,
the stationarity test methods are introduced and the stationarity of actual
pantograph-catenary contact pressure is analyzed in detail. Second, the periodicity
of pantograph-catenary contact pressure data is discussed, including periodic trend
feature, trend term extraction and evaluation of pantograph-catenary contact pres-
sure data. Third, the correlation of pantograph-catenary contact pressure data is
discussed based on EMD (Empirical mode decomposition). In the end, the
high-order statistical properties of pantograph-catenary contact pressure data is
discussedbasedonSK(SpectralKurtosis).InChap.3,thewavemotionvelocityof
contact line considering air damping is deduced in detail and the influence factors
are discussed first. Then the static aerodynamic parameters of contact line are
analyzed. The test experiment of static aerodynamic parameters of contact line is
designed and carried out in the wind tunnel. In the end, the dynamic equations of
vii
viii Preface
pantograph-catenaryconsideringairdampingismodifiedanddiscussed.InChap.4,
the non-contact detection for the height and stagger of contact line is presented in
detail, including the detection value correction of catenary geometric parameters
based on Kalman filtering, the detection correction method of contact line height,
and the detection method based on mean shift and particle filter algorithm. In
Chap. 5, the features of pantograph slipper image including pantograph structure,
slipper type and image features of slipper are given and discussed in detail. First,
the characteristics of pantograph slipper image based on curvelet transform are
analyzed. Second, the pantograph slipper crack extraction based on translational
parallel window in curvelet transform domain is given. In the end, the extraction
algorithm of cracks and experiment verification are performed and verified. In
Chap. 6, some new detection methods of the working state of insulators, clevises,
anddiagonaltubearepresentedanddiscussedindetail.Fortheinsulatordetection,
affineinvariantmoments,fastfuzzymatchingandHarriscornerpointsareadopted
for insulator positioning. Grayscale statistic, wavelet singular value, Chan–Vese
model and curvelet coefficients morphology are adopted for insulator fault detec-
tion. For the clevis fault detection, SIFT (Scale-invariant Feature Transform) and
SUFT (Speed-up Robust Features) are adopted for clevis matching. HOG
(Histogram of Oriented Gradient) features, curvature, Hough transform, Gabor
wavelet transform and second-generation curvelet are adopted for clevis fault
detection.Inaddition,thedetectionofclevispinsispresentedanddiscussed.Inthe
end, the diagonal tube fault detection is presented including the diagonal tube
detection based on cascaded AdaBoost classifier and detection of loosening and
missing of screws from the diagonal tube. In Chap. 7, the time–frequency distri-
butions of pantograph-catenary contact force based on different time–frequency
representations are analyzed and discussed in detail. The detection of contact wire
irregularity in railway catenary is presented. The ZAMD (Zhao–Atlas–Mark dis-
tribution) is presented and discussed as the best candidate for the quadratic time–
frequency representation of pantograph-catenary contact force. In Chap. 8, the
catenaryestimationbasedonthePSDs(PowerSpectrumDensity)ofdynamicdata
ispresentedanddiscussed.First,theselectionofAR(Autoregressive)modelorder
for catenary data is discussed. Then, the correlations between the PSDs and some
crucial operation conditions are investigated and a cross-correlation coefficient is
presentedforevaluatingthepantograph-catenarycouplingperformance.Intheend,
the quantification method of PSDs is developed based on the second-order
polynomial.
Inthisbook,wetrytoindicateanincreasinginterestindetectionandestimation
ofhigh-speedrailwaycatenaryforreal-lifeengineeringapplications.Itishopefulto
provide some references and help for other researchers around the world who
engage in detection and estimation of high-speed railway.
Chengdu, China Zhigang Liu
July 2016
Acknowledgments
ThisbookispartlysupportedbytheNationalNaturalScienceFoundationofChina
(51007074, 51377136, U143420), the New Century Excellent Talent Project of
China Education Ministry (NECT-08-0825), the Sichuan Province Youth Science
and Technology Innovation Team under Grant (2016TD0012), the Key Project of
Science and Technology Research and Development Program of Ministry of
Railways (2011J016-B, 2013J010-B). It is also a summary of the achievements of
ourteamformanyyears,includingdoctoralandmastergraduateresearches,suchas
the works of Dr. Zhiwei Han, Dr. Candidate Hongrui Wang, Ye Han, Guinan
Zhang, M.A. Hongmei Yang, Kunfeng Chen and Xiaoxiao Zhang.
Furthermore, the author would like to thank Dr. Candidate Hongrui Wang, Ye
Han, Zongsheng Zheng, and M.A. Candidate Junwen Chen for their helpful
proofreading.
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