Fault diagnosis for nuclear power equipment based on a recursive principal component analysis approach

Zhao An; Lan Cheng; Yuanjun Guo; Mifeng Ren; Zhile Yang; Wei Feng; Jun Ling; Huanlin Chen; Weihua Chen

doi:10.1117/12.2627200

14 February 2022 Fault diagnosis for nuclear power equipment based on a recursive principal component analysis approach

Zhao An, Lan Cheng, Yuanjun Guo, Mifeng Ren, Zhile Yang, Wei Feng, Jun Ling, Huanlin Chen, Weihua Chen

Proceedings Volume 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021); 121611R (2022) https://doi.org/10.1117/12.2627200
Event: 4th International Conference on Informatics Engineering and Information Science, 2021, Tianjin, China

Abstract

A recursive principal component analysis (RPCA) method was presented in this paper for reliable fault detection of nuclear power systems equipment. Existing fault detection methods for nuclear power equipment are still stay in the theoretical research, as well as experimental analysis. Due to the special working environment of nuclear power equipment, a planned repairs and maintenance for each equipment is a normal operation. However, with the growth in installed capacity of nuclear power units, they suffer from several drawbacks. Offline detection of nuclear power system equipment can never truly reveal the actual situation, leading to maloperation or a waste use of equipment. To tackle such problems, this paper introduces RPCA methods for fault detection. A simple control chart is established for intuitive visualization of the false working condition. A recursive PCA scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. The proposed RPCA approach are verified by detecting abnormal working status occurring in a simulated nuclear power system.

Citation Download Citation

Zhao An, Lan Cheng, Yuanjun Guo, Mifeng Ren, Zhile Yang, Wei Feng, Jun Ling, Huanlin Chen, and Weihua Chen "Fault diagnosis for nuclear power equipment based on a recursive principal component analysis approach", Proc. SPIE 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021), 121611R (14 February 2022); https://doi.org/10.1117/12.2627200

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Safety

Principal component analysis

Mathematical modeling

Failure analysis

Data fusion

Feature selection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years