Robust vision-based displacement measurement and acceleration estimation using RANSAC and Kalman filter

Computer vision(CV)-based techniques have been widely used in the field of structural health monitoring(SHM)owing to ease of installation and cost-effectiveness for displacement measurement.This paper introduces computer vision based method for robust displacement measurement under occlusion by incorporating random sample consensus(RANSAC).The proposed method uses the Kanade-Lucas-Tomasi(KLT)tracker to extract feature points for tracking,and these feature points are filtered through RANSAC to remove points that are noisy or occluded.With the filtered feature points,the proposed method incorporates Kalman filter to estimate acceleration from velocity and displacement extracted by the KLT.For validation,numerical simulation and experimental validation are conducted.In the simulation,performance of the proposed RANSAC filtering was validated to extract correct displacement out of group of displacements that includes dummy displacement with noise or bias.In the experiment,both RANSAC filtering and acceleration measurement were validated by partially occluding the target for tracking attached on the structure.The results demonstrated that the proposed method successfully measures displacement and estimates acceleration as compared to a reference displacement sensor and accelerometer,even under occluded conditions.

Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure

Wind power systems have gained much attention due to the relatively high reliability, maturity in technology and cost competitiveness compared to other renewable alternatives. Advances have been made to increase the power efficiency of the wind turbines while less attention has been focused on structural integrity assessment of the structural systems. Vibration-based damage detection has widely been researched to identify damages on a structure based on change in d^mmic characteristics. Widely spread methods are natural frequency-based, mode shape-based, and curvature mode shape-based methods. The natural frequency-based methods are convenient but vulnerable to environmental temperature variation which degrades damage detection capability; mode shapes are less influenced by temperature variation and able to locate damage but requires extensive sensor instrumentation which is costly and vulnerable to signal noises. This study proposes novelty of damage factor based on sensor fusion to exclude effect of temperature variation. The combined use of an accelerometer and an inclinometer was considered and damage factor was defined as a change in relationship between those two measurements. The advantages of the proposed method are： 1） requirement of small number of sensor, 2） robusmess to change in temperature and signal noise and 3） ability to roughly locate damage. Validation of the proposed method is carried out through numerical simulation on a simplified 5 MW wind turbine model.