AI-Powered Predictive Maintenance: A Comprehensive Review of Deep Learning Approaches for Smart Manufacturing
TL;DR
It is concluded that AI-powered predictive maintenance will become a fundamental component of smart manufacturing and Industry 5.0 initiatives and a conceptual framework integrating AI, IIoT, and deep learning technologies to improve maintenance decision-making is proposed.
Abstract
The rapid emergence of Industry 4.0 technologies has significantly transformed manufacturing industries by integrating Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cloud Computing, Big Data Analytics, and CyberPhysical Systems. Among these advancements, predictive maintenance has emerged as one of the most promising applications for improving operational efficiency and equipment reliability. Traditional maintenance strategies, such as corrective and preventive maintenance, often lead to increased operational costs, unnecessary maintenance activities, and unexpected equipment failures. Consequently, organizations are increasingly adopting AI-powered predictive maintenance systems that utilize deep learning techniques to predict machine failures before they occur. Deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders, Recurrent Neural Networks (RNN), and Transformer-based architectures, have demonstrated remarkable capabilities in analyzing large volumes of industrial sensor data and identifying hidden patterns associated with equipment degradation. This study provides a comprehensive review of AIpowered predictive maintenance using deep learning approaches, examining its applications, benefits, challenges, and future opportunities. The study further proposes a conceptual framework integrating AI, IIoT, and deep learning technologies to improve maintenance decision-making. The findings indicate that deep learning significantly enhances fault diagnosis, Remaining Useful Life (RUL) prediction, anomaly detection, and maintenance optimization. However, challenges such as data quality issues, model interpretability, cybersecurity concerns, and integration complexities continue to influence industrial adoption. The study concludes that AI-powered predictive maintenance will become a fundamental component of smart manufacturing and Industry 5.0 initiatives.