Abstract
This study proposes a deep neural network-based impact locating method on a cantilever beam. The power spectrum of a measured acceleration signal, when an impact is exerted at an arbitrary location on the beam, contains the inherent frequency response of the beam, including resonances and anti-resonances. Especially, the anti-resonances can be a useful feature for estimating the impact location because they are dominated by both the sensor and impact locations. However, in the power spectrum expressed using a linear scale, these anti-resonances may be less noticeable due to their small values relative to the resonances. The proposed impact locating method adopts the power spectrum expressed using a dB scale to highlight the importance of the anti-resonances as the input of a deep neural network. The deep neural network was trained, validated, and tested using a simulated dataset derived from an ideal cantilever beam model with a length of 800 mm, including a single accelerometer. From the test result, the proposed method achieved a root mean square error of about 1 mm in impact locating for a total of 800 impact locations with an interval of 1 mm, a significantly improved accuracy from that using the linear scaled power spectrum.
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This work was supported by the Incheon National University Research Grant in 2019.
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Seokhoon Ryu received his B.S. and M.Sc. from Incheon National University, Korea, in 2015 and 2018, respectively. He is currently working toward his Ph.D. at Embedded Systems Engineering in the same university. His research interests include signal processing, active control of sound and vibration and machine learning.
Jihea Lim received her B.S. in Embedded Systems Engineering from Incheon National University, Korea, in 2021. She is currently in an M.S. candidate at the same department. Her research interests include embedded control systems of autonomous vehicles and mobile robots, and active control of sound and vibration.
Young-Sup Lee received his B.S. from Pusan National University, Korea, in 1987. He then received his M.Sc. and Ph.D. from University of Southampton, United Kingdom, in 1997 and 2000, respectively. He is currently a Professor at Department of Embedded Systems Engineering in Incheon National Univer-sity, Korea. His research interests include robust control of autonomous vehicles and mobile robots, active and robust control, and multimedia signal processing.
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Ryu, S., Lim, J. & Lee, YS. Deep learning-based impact locating using the power spectrum of an acceleration signal on a cantilever beam. J Mech Sci Technol 37, 3365–3377 (2023). https://doi.org/10.1007/s12206-023-0604-5
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DOI: https://doi.org/10.1007/s12206-023-0604-5