Vibration signal separation of rotating cylindrical structure through target-less photogrammetric approach

Vibration analysis is one of the most widely used methodologies for structural health monitoring. For vibration monitoring of structures, contact-type sensors, such as gap sensors, are commonly utilized. However, these devices can add mass loading to a lightweight structure, resulting in degradation in performance of the structure. As an alternative, photogrammetric approaches, such as digital image correlation and point-tracking techniques have been proposed for vibration measurement. Despite the many advantages of image-based techniques applied by non-contact-type sensors, these are ineffective in detecting vibrations at the outer edge of a rotating cylinder-shaped structure because tracking of the speckles or targets on the backside of a structure invisible to a camera is impossible. Furthermore, vibrations occurring in structures are usually attributed to multiple factors; to identify these factors individually, the vibration signals should be detected separately. In this study, we introduce a noncontact-based vibration signal separation technique that can be used to recognize multiple factors that cause a structure to vibrate at different frequency rates. For this purpose, the vibration occurring in a structure is first detected using an edge-based vibration measurement technique. A phase-based motion magnification technique is then used to generate multiple magnified videos at different dominant frequencies. Subsequently, vibrations were detected in multiple magnified videos to separate vibration signals. To demonstrate the effectiveness of the proposed method, quantitative and qualitative assess-ments were conducted on both simulation and real video datasets containing vibrations with multiple frequency rates. The results generated by this method can be utilized for health moni-toring of various types of structures on which targets, necessary for the general photogrammetry approach for monitoring purposes, are not attached. Furthermore, the magnified videos generated in this study can be used for the visualization and mode identification of existing vibration signals at different frequencies in a structure.

Automated summary

Vibration analysis is widely used for structural health monitoring. Traditional contact-type sensors can degrade the performance of lightweight structures, so image-based non-contact sensors have been proposed. This study introduces a non-contact vibration signal separation technique to identify multiple factors causing vibrations at different frequencies in a structure. The technique detects vibrations using an edge-based measurement and uses motion magnification to generate magnified videos at different frequencies for separating vibration signals.