An Explainable and Lightweight Improved 1-D CNN Model for Vibration Signals of Rotating Machinery-Reference-Cited by-同舟云学术

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An Explainable and Lightweight Improved 1-D CNN Model for Vibration Signals of Rotating Machinery

Published:2024-03-01 Issue:5 Volume:24 Page:6976-6997
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Pang Pengfei¹^ORCID,Tang Jian¹^ORCID,Luo Jiqing¹^ORCID,Chen Miao²,Yuan Hui³,Jiang Lei¹

Affiliation:

1. Engineering Institute of Engineering Corps, Army Engineering University, Nanjing, China

2. Mechanical Engineering, Nanjing University of Technology, Nanjing, China

3. Advanced Equipment Manufacturing Research Institute, Nanjing University of Technology, Nanjing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10453373/10413950.pdf?arnumber=10413950

Reference47 articles.

1. An interpretable anti-noise network for rolling bearing fault diagnosis based on FSWT

2. A Novel Multiscale Lightweight Fault Diagnosis Model Based on the Idea of Adversarial Learning

3. Stochastic resonance in multi-stable coupled systems driven by two driving signals

4. Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines

5. Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

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1. Estimating lithium-ion battery capacity from relaxation voltage using a machine learning approach;Journal of Physics: Conference Series;2024-08-01

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