Hybrid Deep Learning Models for Predictive Maintenance in Industrial IoT Systems: Integrating CNNs, LSTMs, and Attention Mechanisms for Smart Factory Optimization

Abstract

Predictive maintenance has emerged as a transformative approach in industrial Internet of Things (IIoT) environments, aiming to forecast equipment failures before they occur and optimize maintenance schedules. Traditional data-driven methods often fall short in handling the complexity and high-dimensionality of industrial sensor data. This paper presents a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms to enhance fault detection accuracy and predictive performance. By leveraging CNNs for spatial feature extraction, LSTMs for temporal pattern learning, and attention modules for contextual prioritization, the proposed architecture enables intelligent decision-making under uncertainty, crucial for modern smart factories. Real-world datasets and synthetic simulations are used to validate the approach. The results indicate a significant improvement in precision, recall, and F1-score over traditional methods. This study contributes to the field of soft computing in industrial systems by offering a robust AI-based model that supports efficient maintenance management in Industry 4.0 environments.

KEYWORDS: Predictive maintenance, Industrial IoT (IIoT), Hybrid deep learning, CNN, LSTM, Attention mechanism, Smart factory, Fault detection, Time-series analysis