Transformer fault diagnosis method based on IFCM-DNN adjudication network

Nan Lin; Shen Dong; Yihong Liu

doi:10.1117/12.2627921

14 March 2022 Transformer fault diagnosis method based on IFCM-DNN adjudication network

Nan Lin, Shen Dong, Yihong Liu

Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 121652D (2022) https://doi.org/10.1117/12.2627921
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China

Abstract

At present, the transformer fault diagnosis methods based on clustering and neural network generally ignore the influence of unbalanced data sets, which could lead the model fall into local optimum. In order to improve the accuracy of transformer fault diagnosis with unbalanced data as samples, a decision network based on improved fuzzy clustering and deep neural network is proposed. A distance correction term is introduced to modify the fuzzy membership function, improve the membership relationship of the boundary data to majority class cluster, and reduce the influence of the wrong division of the boundary data on the cluster center of a minority one, so that the cluster center tends to be stable in the ideal position, and the imbalanced data set is effectively divided, the resolution of data set is improved. In addition, referring to the process of human learning and voting activities, a DNN adjudication network is established after the IFCM. Through learning the partitioned data sets to different degrees, the common network and expert network are used for joint voting, so as to obtain more reliable prediction results. The experimental results show that this method can effectively improve the accuracy of transformer fault diagnosis under unbalanced data sets.

Citation Download Citation

Nan Lin, Shen Dong, and Yihong Liu "Transformer fault diagnosis method based on IFCM-DNN adjudication network", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 121652D (14 March 2022); https://doi.org/10.1117/12.2627921

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
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Transformers

Fuzzy logic

Data centers

Neural networks

Diagnostics

Databases

Feature extraction

RELATED CONTENT

Analysis of classifiers performance for classification of potential microcalcification
Proceedings of SPIE (July 19 2013)

Transformer fault diagnosis model based on FI-CNN method
Proceedings of SPIE (March 14 2022)

Mining neuronal morphology databases using graph transformer
Proceedings of SPIE (June 13 2024)

A computational study on convolutional feature combination strategies for grade...
Proceedings of SPIE (March 01 2017)

Separation of malignant and benign masses using image and segmentation...
Proceedings of SPIE (May 15 2003)

A review of fault prognostics in condition based maintenance
Proceedings of SPIE (November 06 2006)

Instrument classification in polyphonic music based on timbre analysis
Proceedings of SPIE (July 20 2001)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years