Deep Learning-Enabled Industrial Intelligence for Smart Inspection, Equipment Diagnosis, Predictive Maintenance, and Edge Deployment

Abstract

Deep learning has become a central technique in the development of intelligent industrial systems. As manufacturing, energy, transportation, and infrastructure systems generate increasingly large volumes of visual, sensory, temporal, and operational data, conventional rule-based and shallow machine learning methods face limitations in feature extraction, nonlinear modeling, and adaptation to complex operating conditions. Deep learning models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, autoencoders, graph neural networks, and Transformer-based architectures, have been widely investigated for industrial fault diagnosis, visual defect detection, predictive maintenance, process monitoring, remaining useful life estimation, and intelligent production optimization. This review summarizes the major technical directions of deep learning in industrial applications, with emphasis on data characteristics, model architectures, representative tasks, practical deployment constraints, and future research challenges. The paper first discusses the motivation for applying deep learning to industrial intelligence and then reviews its use in industrial visual inspection, rotating machinery fault diagnosis, predictive maintenance, industrial time-series modeling, and edge-based deployment. It further analyzes key challenges, including data imbalance, domain shift, interpretability, real-time requirements, computational cost, and integration with industrial Internet of Things platforms. Finally, the review identifies future research opportunities in self-supervised industrial learning, physics-informed deep learning, multimodal industrial foundation models, trustworthy artificial intelligence, and human-centered industrial decision support.