Abstract
Blast furnace is the core equipment of iron and steel smelting. Traditional inspection mainly relies on manual, the remaining problems include high labor intensity, low efficiency of inspection, inadequate inspection, and difficult digital display of inspection results. With the development of technologies such as UAV and online monitoring and diagnosis and their in-depth application in the field of inspection, firstly, an intelligent inspection business model of “UAV inspection + infrared scanning + data application and visual display” was introduced, then 5G and UAV were applied to temperature measurement of ironmaking blast furnace, blast furnace pipe, hot blast stove. Secondly, in order to realize the safety and stability of blast furnace production, StressWave analysis technology was applied to equipment predictive maintenance, especially for condition monitoring and fault diagnosis. In the specific application case of the gas-seal box and belt conveyor, StressWave on-line condition monitoring system was applied to listen for shock/friction raising events and quantify energy from shock and friction, through comprehensive analysis of on-line condition monitoring data to diagnose fault type and fault severity of gas-seal box and belt conveyor. The accuracy of diagnosis conclusion was verified in the application cases. Finally, this research content and application cases promote application of 5G+ condition monitoring technology in predictive maintenance of ironmaking blast furnace, through effectively improvement of inspection efficiency and quality to provide guarantee for the stable operation of ironmaking blast furnace.
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References
Zhou, J., Chen, R.: Research on digitization of blast furnace in ferrous metallurgy. Shanxi Metall. 45(2), 91–95 (2022). https://doi.org/10.16525/j.cnki.cn14-1167/tf.2022.02.035
Li, Y., Han, Z., Cheng, J.: Design of 5G application system for large special equipment in steel industry. Des. Tech. Posts Telecommun. (7), 66–72 (2021). https://doi.org/10.12045/j.issn.1007-3043.2021.07.016
Xie, Q., Qing, S.: Application practice of 5g communication in the remote operation and maintenance of metallurgical control system. Gansu Sci. Technol. 24, 1–3 (2022). https://doi.org/10.3969/j.issn.1000-0952.2022.24.002
Zhu, X., Zhang, H., Yang, C.: MWPCA blast furnace anomaly monitoring algorithm based on Gaussian mixture model. CIESC J. 72(3), 1539–1548 (2021). https://doi.org/10.11949/0438-1157.20201708
Li, Z., Chu, M., Liu, Z., Li, B.: Prediction and optimization of blast furnace parameters based on machine learning and genetic algorithm. 41(9), 1262–1267 (2020). https://doi.org/10.12068/j.issn.1005-3026.2020.09.008
Zhang, J., Song, X.: Simulation and decision-making system in burden distribution of blast furnace. J. Northeastern Univ. (Nat. Sci.) 10, 1398–1402 (2015). https://doi.org/10.3969/j.issn.1005-3026.2015.10.007
Niu, H., Sun, M., Yang, J.: Application and vision of big data in blast furnace ironmaking. Hebei Metall. 1, 51–55 (2018). https://doi.org/10.13630/j.cnki.13-1172.2018.0112
Steve, S.: Aircraft engine StressWave analysis report. Scientech (2015)
Board, D.B.: StressWave analysis provides early detection of lubrication problems. Swantech LLC (2018)
Wu, T., Chen, S., Wu, P.: Condition monitoring and fault prediction based on StressWave analysis, process automation instrumentation. Chin. J. Sci. Instrum. 12(38), 3061–3070 (2017)
Li, H., Gao, F.: Research on application of stress wave technology in mechanical fault diagnosis of rolling mill unit. Process Autom. Instrum. 40, 60–63 (2020). https://doi.org/10.16086/j.cnki.issn1000-0380.2019030340
Luo, Y., Zhang, X., Kano, M., et al.: Data-driven soft sensors in blast furnace ironmaking: a survey. Front. Inf. Technol. Electron. Eng. 24(3), 327–353 (2023)
Hui, Z., Wang, Q.: Study on the technology of gear drive airtight box for bell-less top in BF. Heavy Mach. 1, 57–60 (2019)
Wang, G., Tang, Z., Lou, S., et al.: Wireless temperature measurement technology application in bell-less top air tight box monitoring. Wide Heavy Plate 10, 45–48 (2014)
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Zhu, M., Gao, F., Guo, L., He, W. (2024). Research and Application of 5G and Condition Monitoring in Predictive Maintenance of Ironmaking Blast Furnace. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_42
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DOI: https://doi.org/10.1007/978-981-97-2757-5_42
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