Emre METİN, Özgür KARAGÜLLE, Abdullah KAYALAK, Mustafa KARAGÜLLE, Ertuğrul ÇAM

Non-Contact Vibration Detection Software and Hardware Design with Image Processing Method to Reveal Substantial Changes in Structures and Systems

Many types of equipment are used for vibration detection in buildings and systems. However, these systems have their own problems. In this article, a combined software and hardware system is designed for the solution of these problems. The system has been designed with the Motion Magnification Analysis (MMA) method, which stands out in the literature studies. C # language and .NET environment have been used as software. The Accord library was used for the image processing process. These libraries were also used for the analysis of subspaces and components of the image. During the study, three new algorithms that have not been applied in the past and not in the literature have been developed. The first of these is the derivative of the method known as Color Magnification, which analyzes the color space. The second is the derivative of the Motion Magnification method, which magnifies the vibration to the visible level. And the third one is the Frequency Determination algorithm, which enables frequency measurement. With this solution, it is provided to make an analysis of the images taken with devices with relatively slow cameras such as mobile phones/tablets as well as fast cameras. All results were compared with a touchable vibration analyzer for benchmarking and showed that the proposed method was successful.

Keywords:

“tireşim, temassız, hareket büyütme analizi,

PDF

___

Ashory, M. R. (1999). High quality modal testing methods (Doctoral dissertation, University of London).
Chen, J. G., Wadhwa, N., Cha, Y. J., Durand, F., Freeman, W. T., & Buyukozturk, O. (2015). Modal identification of simple structures with high-speed video using motion magnification. Journal of Sound and Vibration, 345, 58-71.
Correa, J. C. A. J., & Guzman, A. A. L. (2020). Mechanical Vibrations and Condition Monitoring. Elsevier Science & Technology.
Davis, A., Rubinstein, M., Wadhwa, N., Mysore, G. J., Durand, F., & Freeman, W. T. (2014). The visual microphone: Passive recovery of sound from video
Gonzales, R. (1987). Wintz, Paul. Digital Image Processing.
Hassan, M. A., Malik, A. S., Fofi, D., Saad, N. M., Ali, Y. S., & Meriaudeau, F. (2017). Video-based heartbeat rate measuring method using ballistocardiography. IEEE Sensors Journal, 17(14), 4544-4557.
Li, M., & Lin, J. (2017). Wavelet-transform-based data-length-variation technique for fast heart rate detection using 5.8-GHz CW Doppler radar. IEEE Transactions on Microwave Theory and Techniques, 66(1), 568-576.
Mas, D., Ferrer, B., Acevedo, P., & Espinosa, J. (2016). Methods and algorithms for video-based multi-point frequency measuring and mapping. Measurement, 85, 164-174.
Monkaresi, H., Bosch, N., Calvo, R. A., & D'Mello, S. K. (2016). Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Transactions on Affective Computing, 8(1), 15-28.
Wadhwa, N., Rubinstein, M., Durand, F., & Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4), 1-10.