Bearing Defect Classification Algorithm Based on Autoencoder Neural Network
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-14
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The postproduction defect classification and detection of bearings still relies on manual detection, which is time-consuming and tedious.
To address this, we propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection.
An improved autoencoder is used to reduce dimension feature extraction and reduce large-scale images to small-scale images through encoder dimensional reduction.
Defect classification is completed by feeding the extracted features into a convolutional classification network.
Comparative experiments show that the neural network can effectively complete feature selection and substantially improve classification accuracy while avoiding the laborious algorithm of the conventional method.
American Psychological Association (APA)
Lu, Manhuai& Mou, Yuanxiang. 2020. Bearing Defect Classification Algorithm Based on Autoencoder Neural Network. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1122491
Modern Language Association (MLA)
Lu, Manhuai& Mou, Yuanxiang. Bearing Defect Classification Algorithm Based on Autoencoder Neural Network. Advances in Civil Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1122491
American Medical Association (AMA)
Lu, Manhuai& Mou, Yuanxiang. Bearing Defect Classification Algorithm Based on Autoencoder Neural Network. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1122491
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122491