Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis
Joint Authors
Wang, Guangbin
Wang, Xiaohui
Lv, Ying
Wang, Tengqiang
Cheng, Huanke
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Fault diagnosis is essentially the identification of multiple fault modes.
How to extract sensitive features and improve diagnostic accuracy is the key to fault diagnosis.
In this paper, a new manifold learning method (Mass Laplacian Discriminant Analysis, MLDA) is proposed.
Firstly, it is assumed that each data point in the space is a point with mass, and the mass is defined as the number of data points in a certain area.
Then, the idea of universal gravitation is introduced to calculate the virtual universal gravitation between data points.
Based on the Laplace eigenmaps algorithm, the gravitational Laplacian matrix between the same kind of data and the heterogeneous data is obtained, and the discriminant function is constructed by the ratio of the virtual gravitation between the heterogeneous data and the virtual gravitation between the similar data; the projection function with the largest discriminant function value is the optimal feature mapping function.
Finally, based on the mapping function, the eigenvalues of the training data and the test data are calculated, and the softmax algorithm is used to classify the test data.
Experiments on gear fault diagnosis show that this method has higher diagnostic accuracy than other manifold learning algorithms.
American Psychological Association (APA)
Wang, Guangbin& Lv, Ying& Wang, Tengqiang& Wang, Xiaohui& Cheng, Huanke. 2020. Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis. Shock and Vibration،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1212712
Modern Language Association (MLA)
Wang, Guangbin…[et al.]. Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis. Shock and Vibration No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1212712
American Medical Association (AMA)
Wang, Guangbin& Lv, Ying& Wang, Tengqiang& Wang, Xiaohui& Cheng, Huanke. Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1212712
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212712