Automatic Equipment Status Monitoring and Maintenance Management Based on Fuzzy Logic Decision

As a core component of modern industry, automation equipment has significant differences in operating characteristics, fault types, and aging characteristics compared to traditional primary equipment. Traditional state monitoring methods mainly rely on fixed thresholds for state judgment, but their effectiveness is often unsatisfactory when dealing with problems involving multiple factors, complex systems, and uncertainties. To address this issue, this paper proposes an automated equipment status monitoring and maintenance management method based on fuzzy logic (FL) decision-making. By simulating the way humans handle fuzzy and uncertain problems, this method can more accurately describe the state of equipment, thereby improving the intelligence and efficiency level of industrial automation systems. This not only helps to prevent potential failures, but also optimizes the allocation of maintenance resources, improving the efficiency and effectiveness of maintenance work. The experimental results show that this method has significant advantages in improving monitoring accuracy, reducing failure rates, and optimizing maintenance resource allocation. This method provides a new approach for the intelligence and efficiency of industrial automation systems, and has broad application prospects.