Enhancing Infrastructure Safety: A UAV-Based Approach for Crack Detection

The imperative task of identifying and promptly detecting cracks in concrete bridges is crucial for preserving their structural health and ensuring the safety of users. Traditional bridge inspection methods heavily rely on human eyes and additional tools, demanding extensive training for inspectors and resulting in time-consuming processes. The increasing demand for Unmanned Aerial Vehicles (UAVs) has provided a transformative solution to access hard-to-reach areas efficiently. This research explores the integration of deep learning algorithms, including CNN, RCNN, Fast RCNN, Faster RCNN, and YOLO, to enhance the accuracy and efficiency of UAV-based crack detection systems. Experimental results affirm the effectiveness of these algorithms in addressing challenges such as lighting variations and small crack detection. The study aims to contribute to structural health monitoring, improving maintenance practices, and enhancing safety

Real Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions

Voltage sag is one of the most significant power quality problems in the industry and has a significant impact on induction motor safety and stability. This paper analyzes the characteristics of voltage dips in power systems and induction motors with a special emphasis on balanced dips with the help of virtual grids (regenerative grid simulator), as per IEC 61000-4-11. Three phase induction motors with 3.3 kW, 16 A coupled to a DC generator with 3.7 kW, and 7.8 A rated are considered for the test analysis. This paper aids in the development of an induction motor to achieve improved precision by taking different voltage sags into account. The experimental results benefit the design modifications of induction motors at industrial and other commercial levels of consumers regarding major power quality issues and the behavior of the induction motors. A proposed modification employing ANSYS is provided to further examine the precise performance of induction motors during sag events