Research and application of magnetic field mutation warning technology for seat belts under the coupling effect of multiple physical fields

Abstract When it comes to electric power workers’ safety, the capacity to self-rescue is paramount. The present safety belt system, nevertheless, can only prevent operators from falling to the ground; it is inconvenient for operators to do autonomous rescues and get back on the wire. This research proposes a novel safety belt design concept to enhance electric power workers’ self-rescue abilities. The newly developed safety belt differs most noticeably from the old one in that it incorporates a pedal belt and a new kind of buffer bag, making it easier for operators to shift their position and ascend. Results from testing the safety belt alongside the new buffer bag show that it significantly improves the level of protection for power line workers, in addition to having basic safety protection ability and effectively assisting workers to carry out self-rescue. In order to improve protection with usefulness on building sites, this study describes a smart seat belt that has a power switch and a warning light on the connected seat. The use of electrical seat belts in factories is not, however, covered in any detail. The half effect sensor is used to enhance the protection effectiveness of construction workers by determining the pre-tensioning the force for somatosensory notification reminders and for minimizing the gap in the web of seat belts.From an electromagnetic radiation safety perspective, electric motors may be able to be manufactured in accordance with suitable standards.While the magnet using Convolutional Neural Networks (CNNs) with Multi-Layered Perception (MLP). Power safety belt configuration is also required for high-voltage power lines, therefore it has potential applications in industrial production and manufacturing.In this paper, the suggests a new design of safety belts in high-risk settings, which should include magnetic field sensors and anomaly detectors based on machine learning. The system employs Hall-effect and reed switch sensors to measure the tension of belts and occupancy and machine learning models are used to classify possible failure states by sensor measurements. The experimental findings indicate that the proposed system has a classification accuracy of 97.56% to detect abnormal belt conditions with a loss of 0.001 in validation. The system also makes 30% less response time than the conventional seat belt monitoring systems, which proves to be a great enhancement in safety performance.