A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition

Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper. Firstly, the wear particles are diagnosed and classified by connecting a new joint loss function...

Full description

Saved in:
Bibliographic Details
Published in:Shock and vibration 2022-01, Vol.2022, p.1-10
Main Authors: Zhao, Chunhua, Lin, zhangwen, Tan, Jinling, Hu, Hengxing, Li, Qian
Format: Article
Language:English
Subjects:
Accuracy
Classification
Datasets
Deep learning
Fault diagnosis
Feature extraction
Gear teeth
Gearboxes
Learning
Lubricating oils
Neural networks
Optimization algorithms
Support vector machines
Training
Wear particles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper. Firstly, the wear particles are diagnosed and classified by connecting a new joint loss function and two pretrained models VGG19 and GoogLeNet. Subsequently, the wear particles in gearbox lubricating oil are chosen as the experimental object to make a comparison. Compared with the other four models’ experimental results, the model superiority in wear particle identification and classification is verified. Taking five models as feature extractors and support vector machines as classifiers, the experimental results and comparative analysis reveal that the LCNNE model is better than the other four models because its feature expression ability is stronger than that of the other four models.
ISSN:1070-9622
1875-9203
DOI:10.1155/2022/3696091