Affiliation:
1. Department of Computer and Information Engineering, Hubei Normal University, Huangshi 435000, China
2. Huangshi Bangke Technology Co., Ltd., Huangshi 435000, China
Abstract
<abstract><p>This paper addresses the issue of artificial visual inspection being overly reliant on subjective experience and the difficulty for the human eye to accurately identify dense and non-significant defects. To solve this problem, we have implemented an automatic object detection algorithm based on an improved version of YOLOv5.First, we use the K-means++ clustering algorithm to automatically calculate the Anchor of the model to reduce the effect of the close location of the initial clustering centers on the clustering of the sample data.Second, we add the Coordinate Attention (CA) attention mechanism to the model to allow the model to better capture and understand important features in the images. Then, we add a new detection layer with a downsampling multiplier of 4 to the Neck network to improve the precision of the model. Finally, we use the lightweight network MobileNetV3 instead of YOLOv5's backbone network to reduce the model detection time overhead.Our model achieves 85.87% mAP, which is 6.44% better than the YOLOv5 network, and the detection time for a single image is only 54ms, which is 50% faster than the YOLOv5 network. After testing, we have proven that our proposed algorithm can quickly and accurately detect the condition of bearing appearance defects, improving detection efficiency and reducing costs.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference24 articles.
1. L. Eren, A. Karahoca, M. J. Devaney, Neural network based motor bearing fault detection, in Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No. 04CH37510), 3 (2004), 1657–1660. https://doi.org/10.1109/IMTC.2004.1351399
2. P. K. Kankar, S. C. Sharma, S. P. Harsha, Fault diagnosis of ball bearings using machine learning methods, Expert Syst. Appl., 38 (2011), 1876–1886. https://doi.org/10.1016/j.eswa.2010.07.119
3. C. Tastimur, M. Karakose, I. Aydın, E. Akin, Defect diagnosis of rolling element bearing using deep learning, in 2018 International Conference on Artificial Intelligence and Data Processing(IDAP), (2018), 1–5. https://doi.org/10.1109/IDAP.2018.8620743
4. J. S. Senanayaka, H. V. Khang, K. G. Robbersmyr, Multiple fault diagnosis of electric powertrains under variable speeds using convolutional neural networks, in 2018 XIII International Conference on Electrical Machines(ICEM), (2018), 1900–1905. https://doi.org/10.1109/ICELMACH.2018.8507096
5. C. Sobie, C. Freitas, M. Nicolai, Simulation-driven machine learning: Bearing fault classification, Mech. Syst. Signal Process., 99 (2018), 403–419. https://doi.org/10.1016/j.ymssp.2017.06.025
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献