Abstract
Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.
Highlights
Academic Editor: Francesco BianconiElectrical energy is one of the major pillars of the global economy
Different Non-Destructive Testing (NDT) methods have been proposed in the literature to perform tool inspection based on different scanning technologies [4,5]: thermography imaging, radiography techniques, ultrasonic probes, etc
Database; Data pre-processing module: pre-processes and prepares the input Computed Tomography (CT) data (2D images/3D volume) for the inspection step. This module applies two main pre-processing functions: background subtraction and registration; Defect inspection module: performs an automated defect inspection based on different computer vision and deep-learning-based inspection algorithms: defect classification, defect localization, and defect characterization
Summary
Electrical energy is one of the major pillars of the global economy. It can be generated using different resources such as fossil fuels (coal, natural gas, and petroleum), nuclear energy, and renewable energy sources. The first category uses signal and image processing techniques to extract the defect’s relevant features or pattern This kind of defect inspection is performed using near-netshape production techniques [16] and the kriging model with statistical models to compute the shape deviation errors [17,18]. A new CTIMS-Toolbox is proposed for X-ray Computed Tomography (CT) data inspection. It integrates computer vision techniques and state-of-the-art Artificial. The proposed CTIMS-Toolbox can be used for any other non-destructive testing or inspection application based on X-ray data. It can be used for inspection of aircraft engines, gas and oil industry pipelines, cavity detection in dental diagnosis, etc.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
