Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Ouyang, Hang; Zeng, Jiusun; Li, Yifan; Luo, Shihua

doi:10.3390/pr8040391

Open AccessArticle

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

¹

College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China

²

School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China

^*

Author to whom correspondence should be addressed.

Processes 2020, 8(4), 391; https://doi.org/10.3390/pr8040391

Submission received: 22 February 2020 / Revised: 22 March 2020 / Accepted: 23 March 2020 / Published: 27 March 2020

(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)

Download

Browse Figures

Versions Notes

Abstract

It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

Keywords: gated recurrent unit; support vector data description; time sequence prediction; fault detection and identification

Share and Cite

MDPI and ACS Style

Ouyang, H.; Zeng, J.; Li, Y.; Luo, S. Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network. Processes 2020, 8, 391. https://doi.org/10.3390/pr8040391

AMA Style

Ouyang H, Zeng J, Li Y, Luo S. Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network. Processes. 2020; 8(4):391. https://doi.org/10.3390/pr8040391

Chicago/Turabian Style

Ouyang, Hang, Jiusun Zeng, Yifan Li, and Shihua Luo. 2020. "Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network" Processes 8, no. 4: 391. https://doi.org/10.3390/pr8040391

APA Style

Ouyang, H., Zeng, J., Li, Y., & Luo, S. (2020). Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network. Processes, 8(4), 391. https://doi.org/10.3390/pr8040391

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI