A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis-Reference-Cited by-同舟云学术

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A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis

Published:2024-07 Issue: Volume:246 Page:123124
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Xu Xuefang^ORCID,Bao Shuo^ORCID,Liang Pengfei^ORCID,Qiao Zijian^ORCID,He Changbo^ORCID,Shi Peiming^ORCID

Funder

China Postdoctoral Science Foundation

Natural Science Foundation of Hebei Province

Natural Science Foundation of Anhui Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference59 articles.

1. Opening the black box: Interpretable machine learning for geneticists;Azodi;Trends in Genetics,2020

2. Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection;Buchaiah;Measurement,2022

3. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network;Chen;IEEE Transactions on Instrumentation and Measurement,2017

4. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture;Chen;IEEE transactions on neural networks and learning systems,2017

5. Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples;Chen;IEEE Transactions on Reliability,2022

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