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A real-time monitoring and fault diagnosis method for underground mine electrical automation equipment combined with edge computing

  
24. März 2025

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Kande, M., Isaksson, A. J., Thottappillil, R., & Taylor, N. (2017). Rotating electrical machine condition monitoring automation—A review. Machines, 5(4), 24. KandeM.IsakssonA. J.ThottappillilR. & TaylorN. (2017). Rotating electrical machine condition monitoring automation—A review. Machines, 5(4), 24.Search in Google Scholar

Gonzalez-Jimenez, D., del-Olmo, J., Poza, J., Garramiola, F., & Sarasola, I. (2021). Machine learning-based fault detection and diagnosis of faulty power connections of induction machines. Energies, 14(16), 4886. Gonzalez-JimenezD.del-OlmoJ.PozaJ.GarramiolaF. & SarasolaI. (2021). Machine learning-based fault detection and diagnosis of faulty power connections of induction machines. Energies, 14(16), 4886.Search in Google Scholar

Babak, V. P., Babak, S. V., Eremenko, V. S., Kuts, Y. V., Myslovych, M. V., Scherbak, L. M., ... & Zaporozhets, A. O. (2021). Models and measures for the diagnosis of electric power equipment. Models and Measures in Measurements and Monitoring, 99-126. BabakV. P.BabakS. V.EremenkoV. S.KutsY. V.MyslovychM. V.ScherbakL. M. ... & ZaporozhetsA. O. (2021). Models and measures for the diagnosis of electric power equipment. Models and Measures in Measurements and Monitoring, 99-126.Search in Google Scholar

Nikitin, Y., Božek, P., & Peterka, J. (2020). Logical–linguistic model of diagnostics of electric drives with sensors support. Sensors, 20(16), 4429. NikitinY.BožekP. & PeterkaJ. (2020). Logical–linguistic model of diagnostics of electric drives with sensors support. Sensors, 20(16), 4429.Search in Google Scholar

El Mokhtari, K., & McArthur, J. J. (2024). Autoencoder-Based fault detection using building automation system data. Advanced Engineering Informatics, 62, 102810. El MokhtariK. & McArthurJ. J. (2024). Autoencoder-Based fault detection using building automation system data. Advanced Engineering Informatics, 62, 102810.Search in Google Scholar

Yang, Y., Wu, H., & Ma, J. (2021). Electrical system design and fault analysis of machine tool based on automatic control. International Journal of Automation Technology, 15(4), 547-552. YangY.WuH. & MaJ. (2021). Electrical system design and fault analysis of machine tool based on automatic control. International Journal of Automation Technology, 15(4), 547-552.Search in Google Scholar

Sahu, A. R., Palei, S. K., & Mishra, A. (2024). Data‐driven fault diagnosis approaches for industrial equipment: A review. Expert Systems, 41(2), e13360. SahuA. R.PaleiS. K. & MishraA. (2024). Data‐driven fault diagnosis approaches for industrial equipment: A review. Expert Systems, 41(2), e13360.Search in Google Scholar

Orłowska-Kowalska, T., Kowalski, C. T., & Dybkowski, M. (2017). Fault-diagnosis and fault-tolerant-control in industrial processes and electrical drives. Advanced control of electrical drives and power electronic converters, 101-120. Orłowska-KowalskaT.KowalskiC. T. & DybkowskiM. (2017). Fault-diagnosis and fault-tolerant-control in industrial processes and electrical drives. Advanced control of electrical drives and power electronic converters, 101-120.Search in Google Scholar

Panda, S., Mutallib, M. A., & Dash, B. (2022). Significance of AI in Electrical Control Systems and Automation. International Journal of Advanced Research in Computer and Communication Engineering. PandaS.MutallibM. A. & DashB. (2022). Significance of AI in Electrical Control Systems and Automation. International Journal of Advanced Research in Computer and Communication Engineering.Search in Google Scholar

Arcos, B. P., Bakker, C., Flipsen, B., & Balkenende, R. (2020). Practices of fault diagnosis in household appliances: Insights for design. Journal of Cleaner Production, 265, 121812. ArcosB. P.BakkerC.FlipsenB. & BalkenendeR. (2020). Practices of fault diagnosis in household appliances: Insights for design. Journal of Cleaner Production, 265, 121812.Search in Google Scholar

Chen, R., Li, X., & Chen, Y. (2023). Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action. Protection and control of modern power systems, 8(1), 1-15. ChenR.LiX. & ChenY. (2023). Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action. Protection and control of modern power systems, 8(1), 1-15.Search in Google Scholar

Saad, N., Irfan, M., & Ibrahim, R. (2018). Condition monitoring and faults diagnosis of induction motors: electrical signature analysis. CRC Press. SaadN.IrfanM. & IbrahimR. (2018). Condition monitoring and faults diagnosis of induction motors: electrical signature analysis. CRC Press.Search in Google Scholar

Meng, F., Yang, S., Wang, J., Xia, L., & Liu, H. (2022). Creating knowledge graph of electric power equipment faults based on BERT–BiLSTM–CRF model. Journal of Electrical Engineering & Technology, 17(4), 2507-2516. MengF.YangS.WangJ.XiaL. & LiuH. (2022). Creating knowledge graph of electric power equipment faults based on BERT–BiLSTM–CRF model. Journal of Electrical Engineering & Technology, 17(4), 2507-2516.Search in Google Scholar

Wang, Z., & Zhu, Y. (2023). Fault identification method of electrical automation distribution equipment in distribution networks based on neural network. International Journal of Energy Technology and Policy, 18(3-5), 257-274. WangZ. & ZhuY. (2023). Fault identification method of electrical automation distribution equipment in distribution networks based on neural network. International Journal of Energy Technology and Policy, 18(3-5), 257-274.Search in Google Scholar

You, X. R. (2023, June). Design of Fault Monitoring Algorithm for Electrical Automation Control Equipment Based on Multi-Sensor. In International Conference on Artificial Intelligence and Communication Technology (pp. 279-289). Singapore: Springer Nature Singapore. YouX. R. (2023, June). Design of Fault Monitoring Algorithm for Electrical Automation Control Equipment Based on Multi-Sensor. In International Conference on Artificial Intelligence and Communication Technology (pp. 279-289). Singapore: Springer Nature Singapore.Search in Google Scholar

Samigulina, G., & Samigulina, Z. (2022). Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems. Journal of Intelligent Manufacturing, 33(5), 1433-1450. SamigulinaG. & SamigulinaZ. (2022). Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems. Journal of Intelligent Manufacturing, 33(5), 1433-1450.Search in Google Scholar

Lin, M. (2021). Fault detection method of electrical automation equipment based on neural network. In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems: DPTA 2020 (pp. 1461-1465). Springer Singapore. LinM. (2021). Fault detection method of electrical automation equipment based on neural network. In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems: DPTA 2020 (pp. 1461-1465). Springer Singapore.Search in Google Scholar

PETERKA, J., NIKITIN, Y. R., & BOŽEK, P. (2020). DIAGNOSTICS OF AUTOMATED TECHNOLOGICAL DEVICES. MM Science Journal. PETERKAJ.NIKITINY. R. & BOŽEKP. (2020). DIAGNOSTICS OF AUTOMATED TECHNOLOGICAL DEVICES. MM Science Journal.Search in Google Scholar

Tran, M. Q., Elsisi, M., Mahmoud, K., Liu, M. K., Lehtonen, M., & Darwish, M. M. (2021). Experimental setup for online fault diagnosis of induction machines via promising IoT and machine learning: Towards industry 4.0 empowerment. IEEE access, 9, 115429-115441. TranM. Q.ElsisiM.MahmoudK.LiuM. K.LehtonenM. & DarwishM. M. (2021). Experimental setup for online fault diagnosis of induction machines via promising IoT and machine learning: Towards industry 4.0 empowerment. IEEE access, 9, 115429-115441.Search in Google Scholar

Lu, M., Liu, H., & Yuan, X. (2021). Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion. Traitement du Signal, 38(4). LuM.LiuH. & YuanX. (2021). Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion. Traitement du Signal, 38(4).Search in Google Scholar

Li, X. (2021, April). Application research of power system automation based on electrical automation technology. In Journal of Physics: Conference Series (Vol. 1881, No. 2, p. 022045). IOP Publishing. LiX. (2021, April). Application research of power system automation based on electrical automation technology. In Journal of Physics: Conference Series (Vol. 1881, No. 2, p. 022045). IOP Publishing.Search in Google Scholar

Isaev, A. V., Nefed’ev, A. I., & Isaeva, L. A. (2019, March). Automated system for diagnostics of emergency operation modes of electric motor. In 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) (pp. 1-4). IEEE. IsaevA. V.Nefed’evA. I. & IsaevaL. A. (2019, March). Automated system for diagnostics of emergency operation modes of electric motor. In 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) (pp. 1-4). IEEE.Search in Google Scholar

Dineva, A., Mosavi, A., Gyimesi, M., Vajda, I., Nabipour, N., & Rabczuk, T. (2019). Fault diagnosis of rotating electrical machines using multi-label classification. Applied Sciences, 9(23), 5086. DinevaA.MosaviA.GyimesiM.VajdaI.NabipourN. & RabczukT. (2019). Fault diagnosis of rotating electrical machines using multi-label classification. Applied Sciences, 9(23), 5086.Search in Google Scholar

Chen, H., Liu, Z., Alippi, C., Huang, B., & Liu, D. (2022). Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning. IEEE Transactions on Neural Networks and Learning Systems. ChenH.LiuZ.AlippiC.HuangB. & LiuD. (2022). Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning. IEEE Transactions on Neural Networks and Learning Systems.Search in Google Scholar

Petrović, I., Nikolovski, S., Glavaš, H., & Relić, F. (2020, October). Power System Fault Detection Automation Based on Fuzzy Logic. In 2020 International Conference on Smart Systems and Technologies (SST) (pp. 129-134). IEEE. PetrovićI.NikolovskiS.GlavašH. & RelićF. (2020, October). Power System Fault Detection Automation Based on Fuzzy Logic. In 2020 International Conference on Smart Systems and Technologies (SST) (pp. 129-134). IEEE.Search in Google Scholar

Cheng, Y. (2022). Thermal Fault Detection and Severity Analysis of Mechanical and Electrical Automation Equipment. International Journal of Heat & Technology, 40(2). ChengY. (2022). Thermal Fault Detection and Severity Analysis of Mechanical and Electrical Automation Equipment. International Journal of Heat & Technology, 40(2).Search in Google Scholar

Li, S., & Li, J. (2017). Condition monitoring and diagnosis of power equipment: review and prospective. High Voltage, 2(2), 82-91. LiS. & LiJ. (2017). Condition monitoring and diagnosis of power equipment: review and prospective. High Voltage, 2(2), 82-91.Search in Google Scholar

Zhou, X. (2024, October). The Real-Time Monitoring and Control of Intelligent Sensor Networks in Electrical Automation. In 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC) (pp. 188-191). IEEE. ZhouX. (2024, October). The Real-Time Monitoring and Control of Intelligent Sensor Networks in Electrical Automation. In 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC) (pp. 188-191). IEEE.Search in Google Scholar

Chen, S. C. (2022). Research on Electrical Automation Monitoring System Model of Power Plant Based on CAN Bus. Journal of Electrical and Computer Engineering, 2022(1), 4858826. ChenS. C. (2022). Research on Electrical Automation Monitoring System Model of Power Plant Based on CAN Bus. Journal of Electrical and Computer Engineering, 2022(1), 4858826.Search in Google Scholar

You, Z., Wu, Q., Zhou, H., Ming, J., & Boddu, R. (2023). Fault Detection of Electrical Automation Remote Equipment Based on Data Network. Electrica, 23(3). YouZ.WuQ.ZhouH.MingJ. & BodduR. (2023). Fault Detection of Electrical Automation Remote Equipment Based on Data Network. Electrica, 23(3).Search in Google Scholar

Zhou, L., Cui, Y., & Jain, A. (2022, April). Fault Monitoring Technology of Electrical Automation Equipment Based on Decision Tree Algorithm. In International Conference on Multi-modal Information Analytics (pp. 37-44). Cham: Springer International Publishing. ZhouL.CuiY. & JainA. (2022, April). Fault Monitoring Technology of Electrical Automation Equipment Based on Decision Tree Algorithm. In International Conference on Multi-modal Information Analytics (pp. 37-44). Cham: Springer International Publishing.Search in Google Scholar

Chen, P. (2021). Fault prevention and application of electrical automation equipment based on maintenance methods. In Journal of Physics: Conference Series (Vol. 1750, No. 1, p. 012055). IOP Publishing. ChenP. (2021). Fault prevention and application of electrical automation equipment based on maintenance methods. In Journal of Physics: Conference Series (Vol. 1750, No. 1, p. 012055). IOP Publishing.Search in Google Scholar

Wang, X., McArthur, S. D., Strachan, S. M., Kirkwood, J. D., & Paisley, B. (2017). A data analytic approach to automatic fault diagnosis and prognosis for distribution automation. IEEE Transactions on Smart Grid, 9(6), 6265-6273. WangX.McArthurS. D.StrachanS. M.KirkwoodJ. D. & PaisleyB. (2017). A data analytic approach to automatic fault diagnosis and prognosis for distribution automation. IEEE Transactions on Smart Grid, 9(6), 6265-6273.Search in Google Scholar

Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
1 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere