Abstract:

In smart manufacturing and industrial equipment, AI based predictive maintenance and problem identification help to enhance running efficiency and lower downtime. Classic machine-learning techniques may fail under physical constraints and need huge, labeled datasets. To overcome these limitations, this work investigates Bayesian Inference Models (BIM)—a novel predictive maintenance and fault detection tool. BIM use the predictive capability of AI by merging domain-specific physics-based equations with neural network topologies. This work presents a hybrid BIM architecture combining thermodynamics, fluid dynamics, governing equations of motion, and historical sensor data in rotating machinery with mechanical vibrations. Unlike other data-driven models, BIM forecasts physically compatible predictions with either little or noisy input. The suggested method combines simulated and real-time sensor data using multi-fidelity training to boost generalization across many running circumstances. This program emphasizes robustness and fault diagnostic accuracy devoid of large labeled datasets. According to experimental findings on industrial pump and turbine datasets, the BIM model beats AI approaches in fault classification, predictive maintenance scheduling, and early anomaly detection. The findings imply BIM could assist in pinpointing issues, increase equipment life, and lower false positives. This work shows how BIM using physics-based modeling and data-driven artificial intelligence might enhance industrial maintenance solutions.