A physics-informed deep learning approach for bearing fault detection-Reference-Cited by-同舟云学术

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A physics-informed deep learning approach for bearing fault detection

Published:2021-08 Issue: Volume:103 Page:104295
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Shen Sheng,Lu Hao,Sadoughi Mohammadkazem,Hu Chao,Nemani Venkat,Thelen Adam^ORCID,Webster Keith,Darr Matthew^ORCID,Sidon Jeff^ORCID,Kenny Shawn

Funder

National Science Foundation Directorate for Engineering

Deere and Co

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference69 articles.

1. Advanced bearing diagnostics: A comparative study of two powerful approaches;Abboud;Mech. Syst. Signal Process.,2019

2. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks;Abdeljaber;J. Sound Vib.,2017

3. A hybrid prognostics technique for rolling element bearings using adaptive predictive models;Ahmad;IEEE Trans. Ind. Electron.,2017

4. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis;Azamfar;Mech. Syst. Signal Process.,2020

5. Multi-sensor data fusion using support vector machine for motor fault detection;Banerjee;Inform. Sci.,2012

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