An automatic lightweight feature detection algorithm is developed to perform real-time structural health monitoring (SHM) of large structures. The algorithm works on the specified region of interest (ROI) and applies canny edge detection with k-means clustering for identifying the displaced pixel location in an image sequence. The location of detected edges (white pixels) in the selected ROI is first validated and then given as input to the k-means clustering algorithm for centroid calculation. The pixel movement tracing method is validated by image simulation, indoor digital micrometer experiment and then an outdoor field experiment on wind turbine. The image simulation experiment was performed to generate sample data and ground truth values. In this experiment, the algorithm was able to detect the defined pixel translations. With this validation, other two experiments were conducted. The indoor experiment was implemented for experimental verification where it successfully identifies the moving bar’s 20mm displacement. Likewise, it also accurately measures the natural frequency of the tower of a utility-scale wind turbine. Hence, the algorithm was built on parallel processing with multi-ROI selection to optimize the space and time complexity for real-time vibration analysis. The present study proclaims that the developed algorithm can be used to perform real-time SHM of large-scale structures.
While developing a novel digital image correlation (DIC)-based NDT method, one has to integrate an automatic and robust feature detection method with the DIC technique. Several studies in the past employed various algorithms such as SIFT, SURF and BRISK with DIC for feature detection and correlation initiation purposes. However, our study shows that the performance of available algorithms is subjected to the image of interest from a particular field experiment. Therefore, the selection of the feature detection algorithms is an essential step towards accurate and efficient processing. We have developed a methodology that applies various feature detection algorithms (namely SIFT, SURF, BRISK, ORB and KAZE) and selects the most accurate, efficient and repeatable algorithm for detecting unique natural patterns. Moreover, the methodology is integrated with an in-house 3D-DIC program to identify as well as correlate natural patterns to obtain in-plane and out-of-plane displacements of large structures. The combined methodology is successfully applied and verified by performing field experiments with a light tower of 10m height and a utility-scale wind turbine. It is observed that the developed methodology is robust enough to detect natural patterns accurately and efficiently. It has also been demonstrated that the technique is successful with the determination of 3D displacements and natural frequencies of the large structures.
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