High-Dimensional Time Series Analysis Using Hybrid Deep Learning Architectures for Predictive Maintenance in Industrial Applications
Keywords:
High-dimensional time series, Predictive maintenance, Hybrid deep learning, CNN-LSTM, Industrial IoT, Anomaly detection, Smart manufacturingAbstract
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.
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Copyright (c) 2024 Gayathri Shankar (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
