Recognition of fatigue and severe sliding wear particles using a CNN model with multi-scale feature extractor
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 27 July 2022
Issue publication date: 8 August 2022
Abstract
Purpose
The purpose of this study is to provide a new convolutional neural network (CNN) model with multi-scale feature extractor to segment and recognize wear particles in complex ferrograph images, especially fatigue and severe sliding wear particles, which are similar in morphology while different in wear mechanism.
Design/methodology/approach
A CNN model named DWear is proposed to semantically segment fatigue, severe sliding particles and four other types of particles, that is, chain, spherical, cutting and oxide particles, which unifies segmentation and recognition together. DWear is constructed using four modules, namely, encoder, densely connected atrous spatial pyramid pooling, decoder and fully connected conditional random field. Different from the architectures of ordinary semantic segmentation CNN models, a multi-scale feature extractor using cascade connections and a coprime atrous rate group is incorporated into the DWear model to obtain multi-scale receptive fields and better extract features of wear particles. Moreover, fully connected conditional random field module is adopted for post-processing to smooth coarse prediction and obtain finer results.
Findings
DWear is trained and verified on the ferrograph image data set, and experimental results show that the final Mean Pixel Accuracy is 95.6% and the Mean Intersection over Union is 92.2%, which means that the recognition and segmentation accuracy is higher than those of previous works.
Originality/value
DWear provides a promising approach for wear particle analysis and can be further developed in equipment condition monitoring applications.
Keywords
Acknowledgements
This research was financially supported by the National Key Laboratory of Science and Technology on Helicopter Transmission (Grant Number HTL-A-21G03) (Nanjing University of Aeronautics and Astronautics).
Citation
Liu, X., Cheng, L., Chen, G., Wang, X. and Wang, J. (2022), "Recognition of fatigue and severe sliding wear particles using a CNN model with multi-scale feature extractor", Industrial Lubrication and Tribology, Vol. 74 No. 7, pp. 884-891. https://doi.org/10.1108/ILT-03-2022-0088
Publisher
:Emerald Publishing Limited
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