Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism-Reference-Cited by-同舟云学术

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Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism

Published:2021-04 Issue: Volume:110 Page:379-393
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Xu Zifei,Li Chun,Yang Yang

Funder

Liverpool John Moores University

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference38 articles.

1. Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings;Chen;Appl Math Comput,2014

2. Fostering a renewable energy technology industry: An international comparison of wind industry policy support mechanisms;Lewis;Energy Policy,2007

3. Evaluation and control of loads on wind turbines under different operating conditions by means of CFD;Christoph,2016

4. Study of adaptive blades in extreme environment using fluid-structure interaction method;Miao;J Fluids Struct,2019

5. Shao HD, Jiang HK, Zhang X et al. Rolling bearing fault diagnosis using an optimization deep belief network. Meas Sci Technol 26(11):115002.

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