Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition

Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition

Monitoring the blast furnace shaft static pressure is crucial for maintaining a stable ironmaking process. Traditional rule-based methods and manual inspections suffer from high labor costs and inconsistent standards. This article proposes a new unsupervised anomaly detection framework that combines...

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Bibliographic Details
Main Authors: Xiaodong Sun, Jie Zhu, Bing Tang, Zhaohui Jiang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
blast furnace monitoring
time-series anomaly detection
adversarial autoencoder
variational mode decomposition
unsupervised learning
Online Access:https://www.mdpi.com/1424-8220/25/11/3473
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