An autonomous robot for shell and tube heat exchanger inspection

Abstract

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time‐consuming and labor‐intensive procedure even for an experienced technician. To tackle this challenge, in this study, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task‐specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform algorithm, respectively. Assisted by a guiding cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real‐time analyzed by convolutional neural networks, showing accuracy of nearly 100% for identifying defective and nondefective tubes and 85% for four types of defective tubes and nondefective tubes.

Detection Method for Energy Efficiency Data in Shell-and-Tube Heat Exchangers Using Multi-Pipeline Segmentation Algorithm

“…Lee is interested in discussing the predictability of heat transfer coefficient in STHEs, particularly when the film condensation phenomena are observed [12]. The author tries to correlate four different condensation heat transfer coefficient calculations with experimental data obtained from a steam surface condenser and finds that it is crucial to select appropriate correlations for the most precise prediction, which can be critical when it comes to designing and operating STHEs in the industrial setting.…”

An automated nondestructive testing system for the surface of pressure pipeline welds

“…They used deep learning methods to detect welds and then planned the optimal inspection route based on the distribution characteristics of welds. Zheng et al (2022) developed a machine learning‐based ECT method based on robots for the automatic NDT of shell and tube heat exchangers. Yan et al (2023), using visual images, proposed a semisupervised anomaly detection method for pipeline defect detection.…”