Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information-Reference-Cited by-同舟云学术

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Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Published:2021-01 Issue: Volume:170 Page:108718
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Long Zhuo,Zhang Xiaofei,Zhang Li,Qin Guojun,Huang Shoudao,Song Dianyi,Shao Haidong,Wu Gongping

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference30 articles.

1. A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: state of the art;Liu;ISA Trans,2017

2. Fault diagnosis of rolling element bearing based on symmetric cross entropy of neutrosophic sets;Kumar;Measurement,2020

3. Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects;Kumar;Measurement,2021

4. Detection of broken rotor bar faults in induction motor at low load using neural network;Bessam;ISA Trans,2016

5. Fault detection of broken rotor bar in LS-PMSM using random forests;Quiroz;Measurement,2018

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