Enhancing Measurement Precision for Rotor Vibration Displacement via a Progressive Video Super Resolution Network
Qixuan He, Sen Wang, Tao Liu, Chang Liu, Xiaoqin Liu
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 paper, a progressive video super-resolution 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 video super-resolution 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 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 paper achieves 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 video super-resolution networks, the reconstructed image frames in our network exhibit a smooth vibration displacement curve and minimal deviation.