Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network-Reference-Cited by-同舟云学术

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Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network

Published:2017-05-10 Issue:6 Volume:28 Page:065018
ISSN:0957-0233
Container-title:Measurement Science and Technology
language:
Short-container-title:Meas. Sci. Technol.

Author:

Yuan Haiying,Wang Xiuyu,Sun Xun,Ju Zijian

Funder

Natural Science Foundation of Beijing Municipality

Scholarship sponsored by China Scholarship Council

National Natural Science Foundation of China

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Link

http://stacks.iop.org/0957-0233/28/i=6/a=065018/pdf

Reference35 articles.

1. Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

2. Wavelet Kernel Local Fisher Discriminant Analysis With Particle Swarm Optimization Algorithm for Bearing Defect Classification

3. Second generation wavelet transform for data denoising in PD measurement

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1. Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree;Entropy;2024-02-29

2. Research on a concrete compressive strength prediction method based on the random forest and LCSSA-improved BP neural network;Journal of Building Engineering;2023-10

3. A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations;Ain Shams Engineering Journal;2023-04

4. Prediction of Concrete Compressive Strength Based on the BP Neural Network Optimized by Random Forest and ISSA;Journal of Function Spaces;2022-08-27

5. Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain;Sensors;2022-08-03

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