Research on Virtual Simulation Experiment Teaching of Scraper Conveyor Condition Monitoring and Fault Diagnosis
DOI:
https://doi.org/10.54097/5j3jj754Keywords:
Scraper Conveyor, Virtual Simulation, Condition Monitoring, Fault Diagnosis, Experimental TeachingAbstract
The scraper conveyor status monitoring and fault diagnosis virtual simulation experiment system is designed and developed based on the operating environment of the scraper conveyor in the fully mechanized mining face. Using augmented virtual reality teaching resources, through immersive display and interactive operation experiments, students are guided on basis of scraper conveyor mechanical cognition, master the virtual teaching experiment of state monitoring and fault diagnosis such as broken chain monitoring, motor fault classification and identification, reducer compound fault separation, etc. The teaching practice shows that the system can overcome the harsh production environment in coal mines and the risk factors caused by equipment failure, improve students' understanding of the mechanical fault diagnosis technology, and mobilize students' enthusiasm for experiments and initiative to enhance students' innovative ability.
Downloads
References
G.F. Wang, G.R. Zhao, H.W. Ren. Analysis of key core technologies of smart coal mine and intelligent mining [J]. Journal of Coal, 2019, 44(01): 34-41.
G.Y. Meng, X.H. Cheng. Analysis of current situation and development of mining scraper conveyor technology in China [J]. Coal Engineering, 2014, 46(10): 58-60.
H.W. Fan, X.H. Zhang, X.G. Cao, et al. Research status and prospect of coal mine machinery fault diagnosis in China under the background of smart mine[J]. Vibration and Shock, 2020, 39(24): 194-204.
Q. Zhang, Z.G. Wu, X. Qi, et al. Research on remote dynamic monitoring and fault diagnosis system of scraper conveyor [J]. Instrumentation Technology and Sensors, 2016, No. 400(05): 51-53+60.
W.G. Wang, J.H. Hu, H. Liu. Status and Development of Virtual Simulation Experimental Teaching in Foreign Universities [J]. Laboratory Research and Exploration, 2015, 34 (05): 214-219.
Y.B. Hou, D.P. Yang, Y. Zhang, et al. Virtual simulation experimental teaching on technology of synthesized mining roof coal removal [J]. Experimental Technology and Management, 2020, 37(11): 151-155.
L.M. Cao, S.J. Sun, J.N. Li, et al. Research on virtual simulation experimental teaching of coal mining machine in coal mine working face [J]. Experimental Technology and Management, 2019,36(02): 198-203.
Y.J. Wang, M. Yang, G.L. Guo, et al. Construction and application of mine surveying virtual simulation experimental teaching system [J]. Surveying and Mapping Bulletin, 2016 (07): 129-132.
F. Zhang, W.W. Shang, H.Y. He, et al. Deep learning-based robot grasping virtual simulation experimental teaching system [J]. Experimental Technology and Management, 2022, 39(01): 173-177.
D.S. Zhang, H.Y. Yu, X.B. Zhao, et al. Study on vibration characteristics of scraper conveyor chain polygon effect [J]. Mechanical Strength, 2018, 40(01): 20-26.
B.Y. He, Y.H. Sun, R. Nie, et al. Study on the dynamic behavior of circular chain drive system of mining scraper conveyor [J]. Journal of Mechanical Engineering, 2012, 48(17): 50-56.
L. Yu. Research on the fault monitoring sensor for chain breakage of mining heavy-duty scraper conveyor [J]. Journal of Coal, 2011, 36(11): 1934-1937.
Z.F. Lu, M. Qin, H. Chen, et al. Application of piezoelectric acceleration sensor in vibration measurement system [J]. Instrumentation Technology and Sensors, 2007, No. 293(07): 3-4+9.
G.H. Xue, J. Zhang, X.D. Ji, et al. Research on vibration fault diagnosis of reducer of underground belt conveyor [J]. Industrial and Mining Automation, 2014, 40(06): 51-53.
S. Yuan, C. Shao. Research on fault classification and prediction of coal mining machine based on convolutional neural network [J]. Coal Technology, 2022, 41(11): 227-229.
X.X. Mao, H.J. Wang, F.X. Han, et al. Fault classification and identification method for electromechanical systems based on deep convolutional neural network [J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(02): 87-93.
G.C. Chen, J. Zhang, G.L. Kan. Intelligent fault diagnosis method based on improved superimposed autoencoder bearing [J]. Noise and Vibration Control, 2022, 42(01): 156-161.
G. Yu, Y.Q. Zhou. Single-channel blind source separation algorithm and its application in vibration source analysis of construction machinery [J]. Journal of Mechanical Engineering, 2016, 52(10): 1-8.
Y.M. Hou, R.B. Zhang. Blind separation of single-channel mechanical noise signal based on EEMD [J]. Manufacturing Automation, 2017, 39(11): 134-137.
H.K. Li, X.F. Zhang, F.J. Xu, et al. Blind separation and weak feature extraction of underdetermined signals based on time-frequency analysis [J]. Journal of Mechanical Engineering, 2014, 50(18): 14-22.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Education and Educational Research
This work is licensed under a Creative Commons Attribution 4.0 International License.