Fuzzy Logic-Based Predictive Maintenance in Industry 4.0: Enhancing Sustainability through Uncertainty Modeling
DOI:
https://doi.org/10.46610/JoFSFLD.2025.v02i01.005Keywords:
Fuzzy logic, Industry 4.0, Machine learning, Predictive maintenance, Sustainability, Uncertainty modelingAbstract
The predictive maintenance model represents a key Industrial 4.0 operational element that both minimizes equipment failures and, slashes operational expenses and boosts operational performance. Operating with traditional PdM techniques becomes challenging because of unclear sensor data, unpredictable equipment wear, and dynamic operational settings. The research tackles uncertainty challenges in predictive maintenance through fuzzy logic-based models for uncertainty prediction. The framework of fuzzy logic functions excellently to manage uncertain and unclear data information which works best in industrial sites that face challenges determining exact failure limits. The new approach applies integrated fuzzy logic controllers with machine learning prediction systems, which improve maintenance intervention timings without generating unnecessary false signals. Our model boosts decision-making performance through linguistic variables joined with fuzzy inference systems which results in better resource management and extends equipment lifetime. The research evaluates how fuzzy logic-based PdM contributes to sustainability through prevention of superfluous maintenance work while decreasing power usage and maximizing asset usage. This study introduces three essential elements to predictive maintenance framework development under Industry 4.0: a framework based on fuzzy logic and uncertainty handling and sustainability analysis of maintenance benefits. The predicted outcomes show improvements in forecasting reliability while simultaneously reducing costs and achieving better sustainability accomplishments. The research delivers meaningful recommendations which serve industries regarding intelligent maintenance strategy deployment within operationally dynamic and uncertain conditions.
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