Optimized CNN model for identifying similar 3D wear particles in few samples-Reference-Cited by-同舟云学术

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Optimized CNN model for identifying similar 3D wear particles in few samples

Published:2020-11 Issue: Volume:460-461 Page:203477
ISSN:0043-1648
Container-title:Wear
language:en
Short-container-title:Wear

Author:

Wang Shuo,Wu Tonghai^ORCID,Zheng Peng,Kwok Ngaiming

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Materials Chemistry,Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanics of Materials,Condensed Matter Physics

Reference38 articles.

1. A microfluidic chip and dark-field imaging system for size measurement of metal wear particles in oil;Haiden;IEEE Sensor. J.,2015

2. Three-dimensional reconstruction of wear particle surface based on photometric stereo;Wang;Measurement,2019

3. Full-life dynamic identification of wear state based on on-line wear debris image features;Wu;Mech. Syst. Signal Process.,2014

4. Damage Detection Techniques for Wind Turbine Blades: A Review, Mechanical Systems and Signal Processing;Du,2019

5. Integrated model of bp neural network and cnn algorithm for automatic wear debris classification;Wang;Wear,2019

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2. Wear particle image analysis: feature extraction, selection and classification by deep and machine learning;Industrial Lubrication and Tribology;2024-05-21

3. Research on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism;Journal of Tribology;2024-03-25

4. Investigation on Wear Diagnosis of Aero-Engine Mechanical System Based on Lubricant Wear Particle Analysis;2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou);2023-10-12

5. Bridging the trust gap: Evaluating feature relevance in neural network-based gear wear mechanism analysis with explainable AI;Tribology International;2023-09

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