AI-Driven Predictive Maintenance Framework For Industrial IoT Using Hybrid Deep Learning Models

Abstract

Predictive maintenance (PdM) is a cornerstone of Industry 4.0 strategies to reduce unplanned downtime and maintenance costs. The rapid proliferation of Industrial Internet of Things (IIoT) has transformed traditional manufacturing and industrial operations by enabling real-time monitoring, data-driven decision-making, and automation. This paper proposes an end-to-end AI-driven predictive maintenance framework for industrial IoT (IIoT) environments that combines a hybrid 1D-Convolutional Neural Network (CNN) for spatial/feature extraction with Long Short-Term Memory (LSTM) layers for temporal modelling (CNN–LSTM). The proposed framework integrates advanced sensing, edge computing, cloud-based analytics, and hybrid deep learning techniques to enable real-time condition monitoring and early fault detection in industrial machinery. The system leverages data collected from heterogeneous IIoT devices, including vibration sensors, temperature sensors, pressure gauges, and acoustic monitors, which are transmitted through a secure communication network for processing and analysis. The hybrid model is optimized using adaptive training strategies and validated through extensive experiments using publicly available industrial datasets. The paper highlights the practical feasibility of deploying the model at the edge, enabling real-time decision-making with reduced bandwidth consumption and lower dependence on cloud infrastructure.