Evaluating Key Factors in Autonomous Maintenance Using Grey Relational Analysis A Focus on Weight and Data Analysis

An autonomous maintenance strategy entails operators being in charge of regular equipment maintenance. It seeks to reduce equipment breakdowns and enhance general performance. Operators have daily responsibilities that include cleaning, lubricating, and inspecting equipment. This proactive strategy enables early abnormality detection, permits prompt repairs, and reduces unplanned downtime. Autonomous maintenance promotes a sense of ownership, raises equipment reliability, and makes the best use of maintenance resources by enabling operators to maintain equipment. The potential for autonomous care to transform medical procedures makes it important for study. Autonomous maintenance enhances overall equipment performance by enabling production and maintenance teams to schedule, carry out, and audit routine maintenance work. This method makes it possible to quickly identify and fix issues, which improves the availability, effectiveness, and quality of equipment. Researchers can help to improve maintenance strategies and operational efficiency in a variety of industries by researching the application and efficacy of autonomous maintenance. A mathematical method called Grey Relational Analysis (GRA) is used to evaluate the relationship between a group of variables. Even in the case of uncertainty or insufficient data, it measures the level of resemblance or correlation between variables. GRA enables a more thorough examination by taking into account both the trend and dispersion of the data. GRA assists decision-makers in identifying influencing factors and informing judgements based on the relative relevance of variables by calculating grey correlation coefficients. Weight took the first place and Data Analysis took the last place. In this paper, Autonomous Maintenance Weight took the first place and Data Analysis took the last place.