A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

Abstract

Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

“…The output-only modal analysis algorithm for extracting full-field, high resolution structural dynamics information from video of vibrating structures can be summarized as follows [37], with Figure 1 providing a pictorial representation of these three steps using a video of a vertical cantilever beam as an illustrative example. An additional, accessible summarization of these steps without the compressive sampling component can be found in [38]. After applying these steps to the video the result is high-resolution mode shapes and modal coordinates that can be used to estimate resonant frequencies and damping ratios.…”

“…These techniques show potential to be adequately sensitive and robust to environmental noise under well understood limitations. Mode shapes and natural frequencies have from a structure with vibrational amplitude of only 50 µm at a distance of 1.4 meters, while the imager was traveling at 39 mm/s [38,39]. These techniques can also be used to detect changes in structural stiffness representative of damage that is nearly an order of magnitude smaller than the nearest competitor using an array of contact sensors, resulting in significantly less costly measurements in comparison [40].…”

“…An additional, accessible summarization of these steps without the compressive sampling component can be found in [38]. After applying these steps to the video the result is high-resolution mode shapes and modal coordinates that can be used to estimate resonant frequencies and damping ratios.…”

A Novel Approach for 3D-Structural Identification through Video Recording: Magnified Tracking

“…This was achieved via principal component analysis for dimensionality reduction and subsequent complexity pursuit for blind mode identification. Further extensions of this method includes a technique to obtain modal information from videos with frame-rates lower than the Nyquist frequency [ 24 ], as well as non-ideal operating conditions [ 25 ]. The principle was further adopted in the context of damage detection for cantilever structures using spatial fractal dimension analysis [ 26 ].…”

Monitoring of a controlled space flexible multibody by means of embedded piezoelectric sensors and cameras synergy

“…Sabatini et al (2015) used a visual-based approach to elastic displacement detection and measurements; although acceleration sensors such as PZT patches are maybe the most accurate method, they cannot be used remotely because these are contact-type sensors (Son et al, 2015). The optical method has the advantage of needing no inclusion of ad hoc devices and relevant harness; a single camera is sufficient; recent studies also face the problem of extracting full-field structural dynamics in the presence of arbitrary rigid body motion (Dasari et al, 2018). However, small elastic displacement requires high-resolution cameras, but the image processing algorithms have a computational cost that increases with resolution.…”