Image analyses for video-based remote structure vibration monitoring system

Yang YANG , Xiong (Bill) YU

Front. Struct. Civ. Eng. ›› 2016, Vol. 10 ›› Issue (1) : 12 -21.

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Front. Struct. Civ. Eng. ›› 2016, Vol. 10 ›› Issue (1) : 12 -21. DOI: 10.1007/s11709-016-0313-6
RESEARCH ARTICLE
RESEARCH ARTICLE

Image analyses for video-based remote structure vibration monitoring system

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Abstract

Video-based vibration measurement is a cost-effective way for remote monitoring the health conditions of transportation and other civil structures, especially for situations where accessibility is restricted and does not allow installation of conventional monitoring devices. Besides, video-based system is global measurement. The technical basis of video-based remote vibration measurement system is digital image analysis. Comparison of the images allow the field of motion to be accurately delineated. Such information is important to understand the structure behaviors including the motion and strain distribution. This paper presents system and analyses to monitor the vibration velocity and displacement field. The performance is demonstrated on a testbed of model building. Three different methods (i.e., frame difference method, particle image velocimetry, and optical Flow Method) are utilized to analyze the image sequences to extract the feature of motion. The Performance is validated using accelerometer data. The results indicate that all three methods can estimate the velocity field of the model building, although the results can be affected by factors such as background noise and environmental interference. Optical flow method achieved the best performance among these three methods studied. With further refinement of system hardware and image processing software, it will be developed into a remote video based monitoring system for structural health monitoring of transportation infrastructure to assist the diagnoses of its health conditions.

Keywords

structure health monitoring / velocity estimation / frame difference / PIV / optical-flow method

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Yang YANG, Xiong (Bill) YU. Image analyses for video-based remote structure vibration monitoring system. Front. Struct. Civ. Eng., 2016, 10(1): 12-21 DOI:10.1007/s11709-016-0313-6

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References

[1]

LeBlanc BNiezrecki CAvitabile P. Structural health monitoring of helicopter hard landing using 3D digital image correlation. Health Monitoring of Structural and Biological Systems 2010, Pts 1 and 2, 20107650: 89–98

[2]

Bragge THakkarainen MLiikavainio TArokoski JKarjalaiene P. Calibration of triaxial accelerometer by determining sensitivity matric and offsets simultaneously. In: Proceedings of the 1st Joint ESMAC-GCMAS Meeting. Amsterdam, the Netherlands2006

[3]

Arraigada M. Calculation of displacement of measured accelerations, analysis of two accelerometers and application in road engineering. In: Proceedings of 6th Swiss Transport Research Conference. Monter Verita, Ascona2006

[4]

Hamid M A.,Abdullah-AI-Wadud  MAlam Muhammad  Mahbub.A reliable structural health monitoring protocol using wireless sensor networks. In: Proceedings of 14th International Conference on Computer and Information Technology2011, 601–606

[5]

Kapur JSahoo P KWong A. A new method for gray level perjure Thresholding using the entropy of the histogram. In: Proceeding of 7th International Conference on Computing and Convergence Technology (ICCCT)198529: 273–285

[6]

Kumar S.2D maximum entropy method for image Thresholding converge with differential evolution. Advances in Mechanical Engineering and its Applications20122(3): 289–292

[7]

Bailey D G. Pixel calibration techniques. Proceedings of the New Zealand Image and Vision Computing Workshop1995

[8]

Wereley S TGui L. A correlation-based central difference image correction (CDIC) method and application in a four-roll mill flow PIV measurement. Experiments in Fluids200324(1): 42–51

[9]

Willert C EGharib M. Digital particle image velocimetry. Experiments in Fluids199110(4): 181–193

[10]

Quénot G MPakleza JKowalewski T A. Particle image velocimetry with optical flow. Experiments in Fluids199825(3): 177–189

[11]

Moodley KMurrell H. A color-map plugin for the open source, java based, image processing package, ImageJ. Computers & Geosciences200430(6): 609–618

[12]

Igathinathane CPordesimo L OColumbus E PBatchelor W DMethuku S R. Shape identification and particles size distribution from basic shape parameters using ImageJ. Computers and Electronics in Agriculture200863(2): 168–182

[13]

Ruhnau PKohlberger TSchnorr CNobach H. Variational optical flow estimation for particle image velocimetry. Experiments in Fluids200538(1): 21–32

[14]

Angelini E DGerard O. Review of myocardial motion estimation methods from optical flow tracking on ultrasound data. In: the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology society20061(15): 6337–6340

[15]

Barron J LFleet D JBeauchemin S S. Performance of optical flow techniques. International Journal of Computer Vision199412(1): 43–77

[16]

Rocha F R PRaimundo I M Jr, Teixeira L S G. Direct sold-phase optical measurements in flow systems: a review. Analytical Letters201144(1): 528–559

[17]

Horn B K PSchunck B G. Determining optical flow. Artificial Intelligence198117(1): 185–203

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