Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid
Bibliographic record
Abstract
Reliable and comfortable high-speed railway (HSR) has skyrocketed in popularity as a transportation medium for traveling around the world. High-voltage direct current (HVDC) electrification system has been introduced to the HSR gradually. However, the coexistence of AC and DC systems will last for a long time because AC railway systems are still in the dominant position. A detailed HSR traction system transient model operating under the hybrid AC/DC grid was established in PSCAD/EMTDC. We proposed a real-time fault detection and isolation (FDI) method for the simulated model using neural network (NN). Hierarchical structure of the monitoring system has been employed. Low-level sub-monitors supervised the conditions of their local regions and the top-level monitor collected all the feedback from sub-monitors making the final evaluation of the entire HSR system based on a voting strategy. Both off-line and real-time experiments were conducted to validate the effectiveness of the proposed method. In the experiments, the sub-monitors were designed based on Gated Recurrent Unit (GRU) algorithm and implemented on the Xilinx VCU128 FPGA board. For the off-line experiment, the sub-monitors used the training and testing dataset both from PSCAD/EMTDC to construct the architecture of their individual GRU networks and to verify how great the networks can be. For the real-time task, the sub-monitors interfaced with a real-time HSR system emulator running on the Xilinx VCU118 FPGA board to test the performance in the real-time application. The results proved that our proposed FDI method has the capability of real-time detection and can achieve better accuracy within reasonable time and resource consumption than other NN-based methods. Moreover, the method was capable of standing against noises from measured signals to some extent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".