Mr. Yizhen Chen Profile

Yizhen Chen

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 22 May 2024 Paper

Fault diagnosis of rolling bearing based on deep linear discriminant analysis

Xiongbin Liu, Yizhen Chen, Tianyuan Chen, Peng Han, Dan Wang

Proceedings Volume 13176, 131763A (2024) https://doi.org/10.1117/12.3028973

KEYWORDS: Data modeling, Feature extraction, Vibration, Education and training, Convolutional neural networks, Convolution, Signal processing, Statistical modeling, Windows, Nonlinear optics

Read Abstract +

Rolling bearing is one of the important parts of rotating machinery, the monitoring and timely diagnosis of the running state of bearing is of great significance to ensure the safe operation of equipment. Aiming at the problems of multiple parameters and low recognition efficiency of existing intelligent diagnosis models, a rolling bearing fault diagnosis method based on Deep linear discriminant analysis (Deep LDA) was proposed combining the advantages of linear discriminant analysis and one-dimensional convolutional neural network that can effectively extract discriminant features and Deep feature information. After the one-dimensional original vibration signal is preprocessed, one-dimensional convolutional neural network is directly used to obtain the depth nonlinear characteristics of rolling bearings to avoid information loss when the original signal is converted into frequency domain signals. Using LDA, the model can acquire the ability to learn the potential representation of linear separable, and then extract the discriminant fault feature information from nonlinear fault information, and make the extracted sample feature obtain the best separation effect in the sample space, to achieve accurate classification of rolling bearing fault types. In order to verify the classification effect of Deep LDA, two sets of rolling bearing fault data sets were used to verify the proposed model. Experimental results and comparative analysis show that the proposed model can accurately identify different running states of rolling bearings, and has higher recognition accuracy and diagnosis efficiency.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years