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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722452781826048 |
|---|---|
| author | Xiaodong Sun Jie Zhu Bing Tang Zhaohui Jiang |
| author_facet | Xiaodong Sun Jie Zhu Bing Tang Zhaohui Jiang |
| author_sort | Xiaodong Sun |
| collection | DOAJ |
| description | 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 adversarial autoencoder with variational mode decomposition (VMD). Firstly, using VMD combined with sample entropy calculation and clustering algorithm, the trend, period, and other components of multidimensional signals are extracted, and then these components are integrated into an improved adversarial training autoencoder to detect global and local anomalies. The proposed method has an accuracy of 0.95, a recall rate of 0.91, and an F1 score of 0.93. Which demonstrates the method effectively captures multi-scale anomalies including value bias, morphological changes, and sudden fluctuations, while providing analysts with interpretable anomaly detail diagnosis. |
| format | Article |
| id | doaj-art-3c80ec210c5e44ddb936c6dedd8ad0a4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3c80ec210c5e44ddb936c6dedd8ad0a42025-08-20T03:11:20ZengMDPI AGSensors1424-82202025-05-012511347310.3390/s25113473Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode DecompositionXiaodong Sun0Jie Zhu1Bing Tang2Zhaohui Jiang3School of Automation, Central South University, Changsha 410083, ChinaCISDI Information Technology (Chongqing) Co., Ltd., Chongqing 401122, ChinaCISDI Information Technology (Chongqing) Co., Ltd., Chongqing 401122, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaMonitoring 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 adversarial autoencoder with variational mode decomposition (VMD). Firstly, using VMD combined with sample entropy calculation and clustering algorithm, the trend, period, and other components of multidimensional signals are extracted, and then these components are integrated into an improved adversarial training autoencoder to detect global and local anomalies. The proposed method has an accuracy of 0.95, a recall rate of 0.91, and an F1 score of 0.93. Which demonstrates the method effectively captures multi-scale anomalies including value bias, morphological changes, and sudden fluctuations, while providing analysts with interpretable anomaly detail diagnosis.https://www.mdpi.com/1424-8220/25/11/3473blast furnace monitoringtime-series anomaly detectionadversarial autoencodervariational mode decompositionunsupervised learning |
| spellingShingle | Xiaodong Sun Jie Zhu Bing Tang Zhaohui Jiang Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition Sensors blast furnace monitoring time-series anomaly detection adversarial autoencoder variational mode decomposition unsupervised learning |
| title | Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition |
| title_full | Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition |
| title_fullStr | Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition |
| title_full_unstemmed | Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition |
| title_short | Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition |
| title_sort | automated anomaly detection in blast furnace shaft static pressure using adversarial autoencoders and mode decomposition |
| topic | blast furnace monitoring time-series anomaly detection adversarial autoencoder variational mode decomposition unsupervised learning |
| url | https://www.mdpi.com/1424-8220/25/11/3473 |
| work_keys_str_mv | AT xiaodongsun automatedanomalydetectioninblastfurnaceshaftstaticpressureusingadversarialautoencodersandmodedecomposition AT jiezhu automatedanomalydetectioninblastfurnaceshaftstaticpressureusingadversarialautoencodersandmodedecomposition AT bingtang automatedanomalydetectioninblastfurnaceshaftstaticpressureusingadversarialautoencodersandmodedecomposition AT zhaohuijiang automatedanomalydetectioninblastfurnaceshaftstaticpressureusingadversarialautoencodersandmodedecomposition |