Prediction of operation vibration state of coal mine mechatronics equipment based on data mining
| Autor: | Faxian Jia, Nabamita Deb, Guna Sekhar Sajja |
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| Rok vydání: | 2022 |
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| Zdroj: | Journal of Vibroengineering. 24:1016-1026 |
| ISSN: | 2538-8460 1392-8716 |
| Popis: | In order to explore the prediction of the operation vibration state of coal mine mechatronics equipment, the author proposes a method based on data mining in response to the problems of large amount of data in the operating state of coal mine electromechanical equipment, low data utilization, and slow speed of single-machine massive data mining, using Map Reduce technology, a dual Map Reduce mining prediction framework is proposed, establish a data mining prediction model for the running state of dual Map Reduce, using MapReduce1 to extract features of monitoring data, use MapReduce2 to predict and analyze feature data. Finally, by building the Hadoop platform, reveal the relationship between Hadoop cluster nodes and parallel processing speed, the efficiency of the data mining prediction framework is verified: Perform an experimental comparative analysis of the single prediction model and the proposed AGB combined prediction model, the prediction accuracy of the AGB combined prediction model is verified. |
| Databáze: | OpenAIRE |
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