Image-Based Bolt-Loosening Detection Using a Checkerboard Perspective Correction Method.

In this paper, a new image-correction method for flange joint bolts is proposed. A checkerboard is arranged on the side of a flange node bolt, and the homography matrix can be estimated using more than four feature points, which include the checkerboard corner points. Then, the perspective distortion of the captured image and the deviation of the camera position angle are corrected using the estimated homography matrix. Due to the use of more feature points, the stability of homography matrix identification is effectively improved. Simultaneously, the influence of the number of feature points, camera lens distance, and light intensities are analyzed. Finally, based on a bolt image taken using an iPhone 12, the prototype structure of the flange joint in the laboratory is verified. The results show that the proposed method can effectively correct image distortion and camera position angle deviation. The use of more than four correction points not only effectively improves the stability of bolt image correction but also improves the stability and accuracy of bolt-loosening detection. The analysis of influencing factors shows that the proposed method is still effective when the number of checkerboard correction points is reduced to nine, and the average error of the bolt-loosening detection result is less than 1.5 degrees. Moreover, the recommended camera shooting distance range is 20 cm to 60 cm, and the method exhibits low sensitivity to light intensity.

A novel anti-loosening bolt looseness diagnosis of bolt connections using a vision-based technique.

Bolt looseness detection is a common problem in engineering. Most vision-based detection techniques focus on diagnosing ordinary bolt looseness, i.e., the methods used for diagnosis are based only on the sidelines of nuts. These methods cannot be used for anti-loosening bolt looseness diagnosis because of the simultaneous rotation of screws and nuts. Therefore, a novel anti-loosening bolt looseness diagnosis method based on a vision-based technique is proposed in this paper. First, a regular hexagonal cap was installed on the screw, which can be used as a reference for the nut. Then, to automatically distinguish the hexagonal borders of the screw cap and nut, a new hexagonal border reconstruction algorithm is proposed. Furthermore, the relative rotation angles of the screw cap and nut hexagons can be determined using the sidelines of the reconstructed hexagonal borders of the screw cap and nut. Finally, a novel anti-loosening bolt looseness diagnosis method is established by using the relative rotation angle of the regular hexagonal borders of the screw cap and nut under initial status and loose status. A prototype flange node of the transmission tower was used for experimental verification. The results show that the proposed method can effectively detect the loosening angle of anti-loosening bolts.