Abstract

This paper explores the application of distributed deep learning techniques, coupled with advanced feature engineering, to enhance predictive maintenance capabilities within the Industrial Internet of Things (IIoT) landscape. The increasing volume and velocity of data generated by IIoT devices present significant challenges for traditional predictive maintenance approaches. We propose a novel methodology that leverages the distributed processing capabilities of Apache Spark to handle large-scale sensor data, combined with carefully engineered features derived from time-series analysis and domain expertise. A Long Short-Term Memory (LSTM) network, trained in a distributed manner using TensorFlow on a Spark cluster, is employed to predict equipment failures. The efficacy of the proposed approach is demonstrated through experiments on a simulated industrial dataset, showcasing significant improvements in prediction accuracy and reduced false positive rates compared to conventional methods. The results highlight the potential of distributed deep learning and feature engineering to revolutionize predictive maintenance in IIoT environments, leading to reduced downtime, improved operational efficiency, and cost savings.