Video Motion Magnification for Vibration Measurement in Hydropower Applications
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Abstract
Ensuring the mechanical integrity of hydropower plants requires robust structural health monitoring to detect issues like rotor imbalance and cavitation. Video motion magnification offers a promising non-contact alternative for vibration measurement. This paper presents an experimental comparison of three state-of-the-art algorithms (phase-based, learning-based, and Swin Transformer-based) for quantitative vibration measurement. Rather than evaluating only the final output, a novel framework analyses motion signals across multiple stages of the algorithms' processing pipelines to identify optimal extraction points. The frequency detection capabilities of these algorithms are then evaluated using both industrial and consumer-grade cameras. The focus is on comparing their ability to accurately measure vibrations with different input data quality. The results demonstrate the importance of the quality of the input data on the performance of the algorithm, as the compressed videos from the consumer-grade camera performed significantly worse than the uncompressed videos from the industrial camera. The learning-based method demonstrated the best overall performance, particularly with high-quality video data. This enabled the oscillation frequency to be measured at amplitudes over 70 times smaller than a pixel.
How to Cite
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Video Motion Magnification, Vibration Measurement, Hydropower, Video-based, Non-contact Measurement, Sub-pixel Vibration Analysis, Evaluation Framework
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