Authors:
Anjan Kumar Reddy Ayyadapu

Addresses:
Department of Information Technology, Cloudera Inc., Ashburn, Virginia, United States of America.                                      

Abstract:

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.

Keywords: Predictive Maintenance; Industrial Internet of Things; Deep Learning; Big Data; Time-Series Analytics; Convolutional Neural Networks; Long Short-Term Memory.

Received on: 13/09/2024, Revised on: 18/11/2024, Accepted on: 21/12/2024, Published on: 03/06/2025

DOI: 10.69888/FTSIN.2025.000381

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 2, Pages: 69-79

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