A Differential Enhanced ConvNet for Rotating Machinery Diagnosis Under Strong Noise-Reference-Cited by-同舟云学术

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A Differential Enhanced ConvNet for Rotating Machinery Diagnosis Under Strong Noise

Published:2024-02-01 Issue:3 Volume:24 Page:3026-3035
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Wang Yinjun¹^ORCID,Zhang Zhigang²,Du Yanbin¹,Chen Peng¹,Ding Xiaoxi³^ORCID,Yu Wennian³^ORCID

Affiliation:

1. Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing, China

2. Chongqing Research Institute, China Coal Technology and Engineering Group, Chongqing, China

3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China

Funder

Innovative Research Group of Universities in Chongqing

Science and Technology Research Plan Project

Natural Science Foundation of Chongqing, China

Scientific Research Startup Project for High-Level Talents

Lump-sum System Project of Chongqing Talent Plan

Chongqing Talent Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10418056/10177913.pdf?arnumber=10177913

Reference30 articles.

1. Vibration Characteristics of Rolling Element Bearings with Different Radial Clearances for Condition Monitoring of Wind Turbine

2. Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

3. A Two-Stage Transfer Adversarial Network for Intelligent Fault Diagnosis of Rotating Machinery With Multiple New Faults

4. ConditionSenseNet: A Deep Interpolatory ConvNet for Bearing Intelligent Diagnosis under Variational Working Conditions

5. Manifold Sensing-Based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multiscale Feature Weighted Transfer Network for Unlabeled Rotating Machinery Fault Diagnosis;IEEE Sensors Journal;2024-06-15

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