An equipment fault diagnosis method based on fuzzy support
vector machine

Pengpo Wang; Hongwei Han; Fei Shan; Xianfeng Huang; Yilei Wang; Meng Wu; Xianfeng Zhao; Lan Wang

doi:10.1117/12.2641577

10 November 2022 An equipment fault diagnosis method based on fuzzy support vector machine

Pengpo Wang, Hongwei Han, Fei Shan, Xianfeng Huang, Yilei Wang, Meng Wu, Xianfeng Zhao, Lan Wang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480F (2022) https://doi.org/10.1117/12.2641577
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

When the fuzzy factors is in the sample or noise around the classification surface which is the decisive factor for the summary of support vector machines (SVM), the operation results of SVM will be greatly affected. In this case, the noise must be effectively removed to minimize the impact on the sample. Here, the method of “one class to the rest class” is used to establish fault classifier, and a decision algorithm combining binary tree with fuzzy support vector machine is proposed. In this algorithm, the noise points near the classification surface in the samples are removed by SDWFCM (Spacial distance weighted fuzzy C-Means) method firstly. Then, based on the fuzzy feature representation method, the fuzzy support vector machine is improved by combining the fuzzy membership degree determination method and the basis of the threshold determination after fuzzy programming. The support vector machine can effectively process the composite samples of mixed fuzzy samples, in order to accomplish the fault diagnosis of a certain type of equipment.

Citation Download Citation

Pengpo Wang, Hongwei Han, Fei Shan, Xianfeng Huang, Yilei Wang, Meng Wu, Xianfeng Zhao, and Lan Wang "An equipment fault diagnosis method based on fuzzy support vector machine", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480F (10 November 2022); https://doi.org/10.1117/12.2641577

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