Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning
In the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in han...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/3/480 |
| _version_ | 1797318871402676224 |
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| author | Yanshu Li Jiyou Fei |
| author_facet | Yanshu Li Jiyou Fei |
| author_sort | Yanshu Li |
| collection | DOAJ |
| description | In the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in handling the long-term dependencies of time series data and mining the complexity of spatiotemporal information in the field of mine maintenance. The LODS model integrates the advantages of LSTM, DFNN and STAN, providing a comprehensive method for effective feature extraction and prediction. Through experimental evaluation on multiple data sets, the experimental results show that the LODS model achieves more accurate predictions, compared with traditional models and optimization strategies, and achieves significant reductions in MAE, MAPE, RMSE and MSE of 15.76, 5.59, 2.02 and 11.96, respectively, as well as significant reductions in the number of parameters and computational complexity. It also achieves higher efficiency in terms of the inference time and training time. The LODS model performs well in all the evaluation indexes and has significant advantages; thus, it can provide reliable support for the equipment failure prediction of the mine maintenance robot. |
| first_indexed | 2024-03-08T03:58:41Z |
| format | Article |
| id | doaj.art-6fb045748f264650b6c998267b3f74d8 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T03:58:41Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6fb045748f264650b6c998267b3f74d82024-02-09T15:10:21ZengMDPI AGElectronics2079-92922024-01-0113348010.3390/electronics13030480Construction of Mining Robot Equipment Fault Prediction Model Based on Deep LearningYanshu Li0Jiyou Fei1College of Mechanical and Electrical Engineering, Shanxi Datong University, Datong 037009, ChinaCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, ChinaIn the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in handling the long-term dependencies of time series data and mining the complexity of spatiotemporal information in the field of mine maintenance. The LODS model integrates the advantages of LSTM, DFNN and STAN, providing a comprehensive method for effective feature extraction and prediction. Through experimental evaluation on multiple data sets, the experimental results show that the LODS model achieves more accurate predictions, compared with traditional models and optimization strategies, and achieves significant reductions in MAE, MAPE, RMSE and MSE of 15.76, 5.59, 2.02 and 11.96, respectively, as well as significant reductions in the number of parameters and computational complexity. It also achieves higher efficiency in terms of the inference time and training time. The LODS model performs well in all the evaluation indexes and has significant advantages; thus, it can provide reliable support for the equipment failure prediction of the mine maintenance robot.https://www.mdpi.com/2079-9292/13/3/480deep learningfault detectionrobotic maintenancepredictive modelingmining industrydata analysis |
| spellingShingle | Yanshu Li Jiyou Fei Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning Electronics deep learning fault detection robotic maintenance predictive modeling mining industry data analysis |
| title | Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning |
| title_full | Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning |
| title_fullStr | Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning |
| title_full_unstemmed | Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning |
| title_short | Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning |
| title_sort | construction of mining robot equipment fault prediction model based on deep learning |
| topic | deep learning fault detection robotic maintenance predictive modeling mining industry data analysis |
| url | https://www.mdpi.com/2079-9292/13/3/480 |
| work_keys_str_mv | AT yanshuli constructionofminingrobotequipmentfaultpredictionmodelbasedondeeplearning AT jiyoufei constructionofminingrobotequipmentfaultpredictionmodelbasedondeeplearning |