Predictive Maintenance in Smart Manufacturing: A Deep Learning Approach for Equipment Anomaly Forecasting

Abstract

Automated visual defect detection has become an important technology for improving quality control in modern industrial manufacturing. Traditional manual inspection is costly, inefficient, subjective, and difficult to sustain in high-speed production, while rule-based vision systems often fail under changing lighting conditions, surface variations, and new defect types. This paper reviews the development of AI-powered visual defect detection, focusing on machine learning, convolutional neural networks, and YOLO-based real-time detection methods. It explains how deep learning replaces hand-crafted feature design with data-driven representation learning, enabling more accurate recognition of scratches, cracks, pits, edge defects, and other surface anomalies. The paper further discusses a physics-assisted framework that combines heat conduction modelling for synthetic defect generation, anisotropic diffusion for structure-preserving image preprocessing, and gradient descent analysis for training stabilisation under imbalanced datasets. Although these methods improve detection accuracy, robustness, and real-time performance, challenges remain in defect data scarcity, environmental domain shifts, model interpretability, computational cost, and edge deployment. Future research should focus on lightweight models, open datasets, explainable AI, and physics-informed learning.