Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated Residual Convolutional Neural Network

Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated Residual Convolutional Neural Network

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensio...

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Bibliographic Details
Main Authors: Chang Liu, Haiyang Wu, Gang Cheng, Hui Zhou, Yusong Pang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Sensors
Subjects:
feature extraction
degradation identification
dilated convolution
residual connection
Online Access:https://www.mdpi.com/1424-8220/25/14/4299
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