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Surface Defect Detection of Mining Automation Equipment Based on Convolutional Neural Networks

  
27 nov. 2024
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Tao, X., Zhang, D., Ma, W., Liu, X., & Xu, D. (2018). Automatic metallic surface defect detection and recognition with convolutional neural networks. Applied Sciences, 8(9), 1575. Search in Google Scholar

Rusiński, E., Czmochowski, J., Moczko, P., & Pietrusiak, D. (2017). Surface mining machines: problems of maintenance and modernization. Springer. Search in Google Scholar

Brodny, J., Alszer, S., Krystek, J., & Tutak, M. (2017). Availability analysis of selected mining machinery. Archives of Control Sciences, 27(2), 197-209. Search in Google Scholar

Cao, X. G., Zhang, M. Y., Gong, Y. R., Jia, X. L., & Zhang, R. Y. (2021). Maintenance decision method considering inspection of mining equipment. International Journal of Metrology and Quality Engineering, 12, 21. Search in Google Scholar

Kruczek, P., Gomolla, N., Hebda-Sobkowicz, J., Michalak, A., Śliwiński, P., Wodecki, J., ... & Zimroz, R. (2019). Predictive maintenance of mining machines using advanced data analysis system based on the cloud technology. In Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection-MPES 2018 (pp. 459-470). Springer International Publishing. Search in Google Scholar

Khoshouei, M., Bagherpour, R., Hosseinie, S. H., & Ghodrati, B. (2018). A roadmap for lean maintenance of mining machinery. In First International Conference Mines of the Future, Aachen, Germany, May 23-24, 2018. Verlag Mainz. Search in Google Scholar

Zhang, G., Chen, C. H., Cao, X., Zhong, R. Y., Duan, X., & Li, P. (2022). Industrial Internet of Things-enabled monitoring and maintenance mechanism for fully mechanized mining equipment. Advanced Engineering Informatics, 54, 101782. Search in Google Scholar

Paraszczak, J., Planeta, S., & Szymanski, J. (2018). Performance and efficiency measures for mining equipment. In Mine Planning and Equipment Selection 2000 (pp. 667-672). Routledge. Search in Google Scholar

Angeles, E., & Kumral, M. (2020). Optimal inspection and preventive maintenance scheduling of mining equipment. Journal of Failure Analysis and Prevention, 20(4), 1408-1416. Search in Google Scholar

Brodny, J., & Tutak, M. (2017, December). Application of elements of TPM strategy for operation analysis of mining machine. In IOP conference series: earth and environmental science (Vol. 95, No. 4, p. 042019). IOP Publishing. Search in Google Scholar

Balaraju, J., Govinda Raj, M., & Murthy, C. S. (2020). Performance evaluation of underground mining machinery: A case study. Journal of Failure Analysis and Prevention, 20(5), 1726-1737. Search in Google Scholar

Castilla, J., Fortes, J. C., Dávila, J. M., Melgar, S., & Sarmiento, A. (2018). Predictive maintenance of mining machinery based on vibrational analysis. International Multidisciplinary Scientific GeoConference: SGEM, 18(1.3), 663-668. Search in Google Scholar

Dayo-Olupona, O., Genc, B., Celik, T., & Bada, S. (2023). Adoptable approaches to predictive maintenance in mining industry: An overview. Resources Policy, 86, 104291. Search in Google Scholar

Rihi, A., Baïna, S., Mhada, F. Z., Elbachari, E., Tagemouati, H., Guerboub, M., & Benzakour, I. (2022). Predictive maintenance in mining industry: grinding mill case study. Procedia Computer Science, 207, 2483-2492. Search in Google Scholar

Cao, X. G., Xu, T. B., Zhao, Y. J., Zhao, J. B., & Wang, Y. (2021). A study on the predictive maintenance method of fully mechanized mining equipment based on cost and time factors. Research Square. Search in Google Scholar

Brodny, J., & Tutak, M. (2022). Applying sensor-based information systems to identify unplanned downtime in mining machinery operation. Sensors, 22(6), 2127. Search in Google Scholar

Cao, X., Li, P., & Duan, Y. (2021). Joint decision-making model for production planning and maintenance of fully mechanized mining equipment. IEEE Access, 9, 46960-46974. Search in Google Scholar

Andreeva, L. I., & Krasnikova, T. I. (2020). Integral estimation of the activity of the maintenance department of the mining company. In IOP Conference Series: Materials Science and Engineering (Vol. 709, No. 4, p. 044044). IOP Publishing. Search in Google Scholar

Zhang, Y., & Sun, G. (2020, June). Informationization and Big Data Technology in Management and Maintenance of Mining Equipment in Large Open Pit Mines. In Journal of Physics: Conference Series (Vol. 1574, No. 1, p. 012106). IOP Publishing. Search in Google Scholar

Khoshkerdar, M., Saeedi, R., Bagheri, A., Hajartabar, M., Darvishi, M., & Gholamnia, R. (2024). Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive Maintenance. Journal of Health and Safety at Work, 14(1), 92-104. Search in Google Scholar

Shi Hui,Lai Rui,Li Gangyan & Yu Wenyong. (2022). Visual inspection of surface defects of extreme size based on an advanced FCOS. Applied Artificial Intelligence(1). Search in Google Scholar

Zhang Bin,Fang Shuqi & Li Zhixi. (2021). Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD. Complexity. Search in Google Scholar

Yang Yutu,Wang Honghong,Jiang Dong & Hu Zhongkang. (2021). Surface Detection of Solid Wood Defects Based on SSD Improved with ResNet. Forests(10),1419-1419. Search in Google Scholar

Bing Hu & Jianhui Wang. (2020). Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network. IEEE Access108335-108345. Search in Google Scholar