Deep Learning Models for Predictive Maintenance in Industrial IoT with Big Data Support

Combine IIoT and Big Data analytics to revive predictive maintenance as the leading trend, based on deep learning. This work presents deep learning platforms for real-time and historical sensor data monitoring, prediction, and device failure avoidance. Continuous equipment monitoring ensures maximum uptime, productivity, and asset life via predictive maintenance (PdM). To process real-time large-scale high-frequency IIoT data, it combines CNNs, LSTMs, and Autoencoders with Apache Hadoop and Spark. Integrating data ingestion, preparation, training, and deployment creates a resilient architecture. An experimental assessment utilizing an easily accessible industrial dataset confirms the model's accuracy, recall, and F1-score for robust anomaly identification and early failure prediction. A binned histogram displays the data distribution, and a waterfall graphic illustrates the failure impact. The paper defines the model's scalability, its advantages, and the mitigation of defects such as data quality issues, model drift, and delays in real-time decision-making. Addressing gaps using federated learning, edge AI, and simulated data are future research areas. This article presents a smart, scalable, industrial architecture enabling industrial industries to migrate from reactive maintenance to data-driven, proactive technologies using deep learning and big data platforms.