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A Comprehensive Dataset for Deep Learning-Based Monitoring and Analysis in Real Process Control Networks
- Citation Author(s):
- Submitted by:
- Eddison Jaggernauth
- Last updated:
- Wed, 09/11/2024 - 13:54
- DOI:
- 10.21227/ycks-8829
- Data Format:
- Research Article Link:
- License:
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Abstract
This article presents a dataset collected from a real process control network (PCN) to facilitate deep-learning-based anomaly detection and analysis in industrial settings. The dataset aims to provide a realistic environment for researchers to develop, test, and benchmark anomaly detection models without the risk associated with experimenting on live systems. It reflects raw process data from a gas processing plant, offering coverage of critical parameters vital for system performance, safety, and process optimization.
Dataset Files
- Process Control Network Data based on production Gas Plant.zip (261.66 MB)
- Sample code - Deep-Learning-Based Monitoring and Analysis in Real Process Control Networks (DLMAPCN).txt (7.77 kB)