METROLOGICAL CONTROL OF SENSORS FOR MONITORING WORKING CONDITIONS USING ARTIFICIAL INTELLIGENCE - Krainiuk, O.; Buts, Yu.; Didenko, N.; Barbashyn, V.; Trishyna, O. (Municipal economy of cities. Series: «Economy science», 2024)
Metrological control plays a vital role in ensuring the accuracy and reliability of the data collected as part of the working conditions monitoring. It helps to prevent potential errors and guarantee the quality of the results, which is critical for the efficient assessment and management of occupational health and safety. The article aims to investigate and analyse the role and importance of metrological control of sensors in the system for monitoring working conditions at production facilities using artificial intelligence. The article examines the possibilities of using artificial intelligence (AI) to optimise metrological control and analysis of sensor data. The authors provide specific applications of AI to improve the metrological control of sensors and identify the advantages and challenges of introducing AI into the metrological control system at production facilities. These tasks will help to reveal the essence and potential of using AI in the metrological control of sensors for monitoring working conditions and emphasise its significance in improving the safety of workers. Using artificial intelligence to improve the accuracy of sensor measurements in monitoring working conditions helps to increase the efficiency and safety of production processes and reduce health risks for employees. The metrological control methodology is essential for ensuring the reliability of sensor and measuring device measurements. Applying machine learning algorithms to develop sensor calibration models can automate and optimise the processes of measuring working conditions, improving the accuracy and reliability of data. The proposed flowchart demonstrates an innovative approach to calibrating a sound level meter using artificial intelligence (AI). The results show that integrating AI into the occupational health and safety management system contributes to monitoring process automation, predicting risks and hazards to employee health, and optimising safety processes. These approaches can enhance the production processes’ efficiency, safety, and productivity.