This paper discusses a potential framework for an AI-driven predictive maintenance system that can be able to detect belt misalignments in conveyor belt systems in the food and beverage manufacturing industry. In the fast-moving consumption goods (FMCG) sector, conveyor belt systems are important for efficient and safe production, but they often experience unplanned breakdown due to belt drift or misalignment. This can lead to downtime and safety risks. To address this, this research paper proposes an AI approach that combines sensor monitoring, data preprocessing, and predictive modelling using AI models. A sample dataset from Kaggle, a public data library, was used study key parameters such as vibrations, motor current, load, and belt tension. A random forest classifier was used as a proof of concept, predicting misalignment events with 88% accuracy, 85% precision, and 86% recall. The framework has five main layers; data collection, data preprocessing, AI model interface, performance evaluation, and decision support. So even without using real-time industrial data, the results show that AI-based predictive maintenance is feasible in resource-limited settings like Papua New Guinea. This framework offers a practical basis for local industries to reduce unplanned downtime, improve overall production efficiency and take the first step to Industry 4.0.

Key Words: AI-Driven, Conveyor, Framework, Predictive Maintenance, Random Forest

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Ms. Aurora Takendu