Haijian Qin

Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum‐visual‐sensing acquisition system and an image‐processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut‐off composite dimming and near‐infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high‐speed charge‐coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image‐processing fusion model based on the spatial information fuzzy C‐means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image‐processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual‐sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM‐Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.

A clustering similarity particle filter based on state trajectory consistency is presented for the mathematical modeling, performance estimation, and smart sensing of nonlinear systems. Starting from an information fusion model based on the consistency principle of the spatial state trajectory, the predicted observation information of the current particle filter (original trajectory) and future multistage Gaussian particle filter (modified trajectory) are selected as the state trajectories of the sampling particles. Clustering similarity methods are used to measure the state trajectories of the sampling particles and the actual system (reference trajectory). The importance weight of a first‐order Markov model is updated with the measurement results. By integrating the targeted compensation scheme of the latest measurement information into the sequential importance sampling process, the adverse effects of the particle degradation phenomenon are effectively reduced. The convergence theorems of the improved particle filter are proposed and proved. The improved filter is applied to practical cases of nonlinear process estimation, economic statistical prediction, and battery health assessment, and the simulation results show that the improved particle filter is superior to traditional filters in estimation accuracy, efficiency, and robustness.