High-Dimensional Time Series Analysis Using Hybrid Deep Learning Architectures for Predictive Maintenance in Industrial Applications

Abstract

In the era of Industry 4.0, predictive maintenance (PdM) has evolved into a vital component of smart manufacturing systems. The increasing availability of sensor data from industrial equipment introduces challenges related to high-dimensional time series, which necessitate robust analytical frameworks. This study investigates hybrid deep learning architectures, integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Autoencoders, to tackle the curse of dimensionality and temporal dependencies in predictive maintenance. Through a review of contemporary approaches and a curated benchmark dataset, we assess model efficiency, dimensionality reduction, and anomaly detection capabilities. The findings emphasize that hybrid models outperform single-model architectures in capturing both spatial and sequential patterns, ultimately reducing equipment downtime and optimizing operational costs.