Digital Safety Monitoring System for Auto Repair Company

Introduction. The scientific literature discusses the potential of artificial intelligence (AI) for ensuring industrial safety. Risk control methods are considered and recommendations for incident prevention are given. The relationship between the competencies of lifting crane operators and the probability of accidents has been studied. Examples of using neural networks for determining the reliability of removable lifting devices are presented. Remote monitoring of operational safety is described. However, the use of AI to manage risks in a car repair station has not been sufficiently studied. This research aims to address this gap. The aim of this work is to demonstrate the potential of neural networks in creating a safety monitoring system for an automobile repair facility. Materials and Methods. The design materials of the service station at the equipment repair and maintenance center served as basic information. This enterprise was created by specialists of the Department of Operation of Transport Systems and Logistics at the Don State Technical University (DSTU). Risks were classified according to GOST ISO 12 100 and GOST R 58 771. Neural networks were trained using open-source libraries for the Python programming language. The digital monitoring system model with visualization was implemented using the AnyLogic simulation system. Results. The authors of this work trained 20 neural networks and selected five with the lowest error function values (from 74% to 78%). Out of five networks that worked most correctly, one was chosen that predicted the output parameter more accurately — 74%. The neural network with the best performance was a multilayer perceptron with 30 neurons in the input layer, 15 in the hidden layer, and 3 in the output layer. It was used to create a digital twin that warned in real time about potentially dangerous events: the movement of a car, a crane, and the opening of an inspection pit. Additionally, it identified workers without personal protective equipment or access to the work area. Discussion and Conclusion. The use of a digital safety monitoring system model will make it possible to identify high-risk work areas in advance, and reduce accidents and industrial injuries. The introduction of this model in auto repair facilities involves the installation of sensors and warning systems. In the future, we plan to explore the possibility of integrating algorithms with the risk monitoring system to help personnel repair specific types of machines.