Optimization of tower crane operation using automated control systems
Abstract
The article discusses the process of optimizing the operation of a tower crane through the implementation of automated control systems. The main advantages of automation are analyzed, including improved crane efficiency, reduced risks of human errors, and decreased maintenance costs. Special attention is given to modern technologies that enable precise control of lifting and load-moving processes, as well as the integration of monitoring and equipment diagnostics systems. The implementation of such systems significantly improves productivity, ensures safety, and reduces the impact of the human factor on the operation of the tower crane.
The paper also examines the impact of the human factor on the performance of tower cranes and the potential for its reduction through the introduction of machine vision systems. The risks associated with human errors and their effect on safety and productivity are analyzed. The principles of operation of machine vision systems, their technical capabilities, and advantages in crane control automation are outlined, particularly for monitoring the work zone, determining safe load-moving trajectories, and minimizing collision risks. Methods for implementing these systems using cameras, sensors, and deep learning algorithms are discussed. The effectiveness of algorithms, such as convolutional neural networks (CNN), and the technical challenges arising from the integration of these technologies are explored. The results demonstrate that machine vision systems improve safety, reduce operator workload, and enhance crane operation efficiency.
The introduction of machine vision systems is an important step towards improving safety, increasing efficiency, and reducing the workload on operators during tower crane operations. Furthermore, these systems can define safe load-moving trajectories, minimizing the potential for collisions or damage.
Key words: tower crane, accidents, human factor, monitoring, machine vision, drowsiness, artificial intelligence, security system.
