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Enhancing Measurement Precision for Rotor Vibration Displacement via a Progressive Video Super Resolution Network | IEEE Journals & Magazine | IEEE Xplore

Enhancing Measurement Precision for Rotor Vibration Displacement via a Progressive Video Super Resolution Network


Abstract:

Recent years have seen the widespread utilization of vision-based noncontact methods for measuring rotor vibrations, but the measurement accuracy of such approaches is st...Show More

Abstract:

Recent years have seen the widespread utilization of vision-based noncontact methods for measuring rotor vibrations, but the measurement accuracy of such approaches is still substantially constrained by both the acquisition environment and the equipment, for which improving the quality and clarity of the captured sequence frames would be an effective solution strategy. In this article, a progressive video super-resolution (VSR) reconstruction network is thus constructed to enhance the image feature information during the preliminary phase of vibration displacement measurement, elevating the measurement accuracy while increasing the capture accuracy of the object detection algorithm. To address the challenge of the impractical application of VSR reconstruction methods in diverse industrial conditions, our approach employs pixel displacements between adjacent frames as a reference for motion estimation, ensuring effective feature alignment through a prealignment module. Additionally, a deep feature extraction module is implemented to capture long-range dependencies in multiscale feature representations, crucial for preserving structural image information. To further enhance reconstruction optimization, a feature fusion module (FFM) is introduced, integrating information from diverse rotor images. The experimental results demonstrate that the proposed network surpasses current advanced multiple comparison networks in reconstructing rotor datasets across diverse conditions and rotational speeds and achieves this with a modest parameter count and short run-time, striking a trade-off between computational cost and performance. Specifically, the network proposed in this article achieves peak signal-to-noise ratio (PSNR) values of 41.07, 26.11, 25.05, and 44.96 respectively, with less than half the parameter count of BasicVSR++ across four distinct rotor datasets. In comparison to other VSR networks, the reconstructed image frames in our network exhibit a smooth vibration d...
Article Sequence Number: 6005113
Date of Publication: 25 March 2024

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