Leveraging Distributed Deep Learning and Feature Engineering for Enhanced Predictive Maintenance in Industrial IoT Big Data

Abstract

The advent of the Industrial Internet of Things (IIoT) has led to an explosion of sensor-generated data within industrial environments, creating new opportunities and challenges for predictive maintenance (PdM). Traditional statistical and machine learning approaches often fall short in addressing the scale, complexity, and real-time demands of IIoT data. This paper introduces a novel, scalable framework that integrates distributed deep learning with advanced feature engineering to enhance PdM performance. Leveraging Apache Spark for large-scale data processing and TensorFlow for distributed training, we develop a Long Short-Term Memory (LSTM) model tailored for time-series prediction of equipment failures. Our methodology extracts meaningful features from raw sensor data to improve model accuracy and reliability. Experimental evaluation on a simulated industrial dataset demonstrates that our approach significantly outperforms conventional PdM techniques in prediction accuracy, false positive reduction, and operational efficiency. The proposed framework illustrates the transformative potential of distributed deep learning and feature engineering in realizing robust PdM solutions for IIoT-driven industries.