A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data-Reference-Cited by-同舟云学术

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A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data

Published:2024-02 Issue: Volume:226 Page:114171
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Wang Changdong,Yang Jingli^ORCID,Zhang Baoqin

Publisher

Elsevier BV

Reference43 articles.

1. A mechanical part fault diagnosis method based on improved multiscale weighted permutation entropy and multiclass LSTSVM;Zhou;Measurement,2023

2. A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions;Liu;Measurement,2023

3. A multi-branch convolutional transfer learning diagnostic method for bearings under diverse working conditions and devices;Wang;Measurement,2021

4. Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm;Gai;Measurement,2021

5. Thermographic fault diagnosis of electrical faults of commutator and induction motors;Glowacz;Eng. Appl. Artif. Intel.,2023

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2. A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions;Reliability Engineering & System Safety;2024-12

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4. SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer;Advanced Engineering Informatics;2024-10

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