Research on TPC system fault diagnosis method based on the multi-layer ATT-GRU network model

Haitao Wang; Jian Lan; Shiqi Han; Hongbing Tao

doi:10.1117/12.2625048

6 December 2021 Research on TPC system fault diagnosis method based on the multi-layer ATT-GRU network model

Haitao Wang, Jian Lan, Shiqi Han, Hongbing Tao

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 1208510 (2021) https://doi.org/10.1117/12.2625048
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

The torpedo car (TPC) plays a vital role in the safe transportation of molten iron in steel manufacturing. Due to its longterm operation in high-temperature environments and frequent use, it is prone to a variety of faults. However, traditional manual judgment methods are not effective in fault diagnosis and have low safety factors. Through engineering practice and data visualization analysis, it is found that the temperature data of the torpedo car have a strong time dependence and are closely related to the occurrence of its faults. Therefore, this paper selects the temperature data from the working status data of a torpedo car in a large steel plant. On the basis of effective data preprocessing and sample construction, the multilayer ATT-GRU network model based on the attention mechanism using timing features is proposed for its fault diagnosis. The model uses a multilayer GRU network to extract the temperature sequence characteristics of different time segments, and the attention mechanism is used to focus on the part of the input that contributes a greater amount to the fault diagnosis results, so as to improve the diagnostic effect of the model on the type of the torpedo car faults. Compared with traditional methods, experiments show that this method significantly improves the convergence speed of model training and diagnosis accuracy. It provides a friendly solution for real-time and accurate fault diagnosis of the torpedo car and other industrial equipment.

Citation Download Citation

Haitao Wang, Jian Lan, Shiqi Han, and Hongbing Tao "Research on TPC system fault diagnosis method based on the multi-layer ATT-GRU network model", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 1208510 (6 December 2021); https://doi.org/10.1117/12.2625048

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Statistical modeling

Databases

Visualization

Clouds

Iron

Visual process modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years