Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations
Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault pr...
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MDPI AG
2024-09-01
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| author | Jing Xiong Youchao Sun Junzhou Sun Yongbing Wan Gang Yu |
| author_facet | Jing Xiong Youchao Sun Junzhou Sun Yongbing Wan Gang Yu |
| author_sort | Jing Xiong |
| collection | DOAJ |
| description | Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm. |
| format | Article |
| id | doaj-art-74fced56ba5948778fd459a23e9809fa |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-74fced56ba5948778fd459a23e9809fa2025-08-20T01:55:58ZengMDPI AGApplied Sciences2076-34172024-09-011418815610.3390/app14188156Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit StationsJing Xiong0Youchao Sun1Junzhou Sun2Yongbing Wan3Gang Yu4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaSILC Business School, Shanghai University, Shanghai 201800, ChinaShanghai Rail Transit Technology Research Center, Shanghai 201103, ChinaSILC Business School, Shanghai University, Shanghai 201800, ChinaMechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm.https://www.mdpi.com/2076-3417/14/18/8156platform screen door systemfault predictionsparse and weak feature datadata augmentationCNN-LSTM |
| spellingShingle | Jing Xiong Youchao Sun Junzhou Sun Yongbing Wan Gang Yu Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations Applied Sciences platform screen door system fault prediction sparse and weak feature data data augmentation CNN-LSTM |
| title | Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations |
| title_full | Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations |
| title_fullStr | Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations |
| title_full_unstemmed | Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations |
| title_short | Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations |
| title_sort | sparse temporal data driven ssa cnn lstm based fault prediction of electromechanical equipment in rail transit stations |
| topic | platform screen door system fault prediction sparse and weak feature data data augmentation CNN-LSTM |
| url | https://www.mdpi.com/2076-3417/14/18/8156 |
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