Data-driven multivariate time series prediction of in-vehicle equipment failure rates
Abstract Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited p...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-024-00543-2 |
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| author | Yongfei Guo Yonggang Chen Haiyong Wang Yang Liu |
| author_facet | Yongfei Guo Yonggang Chen Haiyong Wang Yang Liu |
| author_sort | Yongfei Guo |
| collection | DOAJ |
| description | Abstract Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance. |
| format | Article |
| id | doaj-art-e04b6e7a79b84935b42aab843a8d5075 |
| institution | Kabale University |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-e04b6e7a79b84935b42aab843a8d50752024-11-10T12:27:41ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122024-11-0171112210.1186/s44147-024-00543-2Data-driven multivariate time series prediction of in-vehicle equipment failure ratesYongfei Guo0Yonggang Chen1Haiyong Wang2Yang Liu3College of Automation and Electrical Engineering, Lanzhou Jiaotong UniversityCollege of Automation and Electrical Engineering, Lanzhou Jiaotong UniversityCollege of Electronic and Information Engineering, Lanzhou Jiaotong UniversityChina Railway Xi’an Bureau Group CoAbstract Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.https://doi.org/10.1186/s44147-024-00543-2Vehicle-mounted equipmentFailure rate predictionMultivariate variational modal decompositionGraph neural networkTransformer |
| spellingShingle | Yongfei Guo Yonggang Chen Haiyong Wang Yang Liu Data-driven multivariate time series prediction of in-vehicle equipment failure rates Journal of Engineering and Applied Science Vehicle-mounted equipment Failure rate prediction Multivariate variational modal decomposition Graph neural network Transformer |
| title | Data-driven multivariate time series prediction of in-vehicle equipment failure rates |
| title_full | Data-driven multivariate time series prediction of in-vehicle equipment failure rates |
| title_fullStr | Data-driven multivariate time series prediction of in-vehicle equipment failure rates |
| title_full_unstemmed | Data-driven multivariate time series prediction of in-vehicle equipment failure rates |
| title_short | Data-driven multivariate time series prediction of in-vehicle equipment failure rates |
| title_sort | data driven multivariate time series prediction of in vehicle equipment failure rates |
| topic | Vehicle-mounted equipment Failure rate prediction Multivariate variational modal decomposition Graph neural network Transformer |
| url | https://doi.org/10.1186/s44147-024-00543-2 |
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