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M2BIST-SPNet: RUL prediction for railway signaling electromechanical devices

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Abstract

Railway signaling electromechanical devices (RSEDs) play a pivotal role in the railway industry. Normal wear and tear of these devices occur during day-and-night operation and even progressively develop into failures. Hence, it is imperative to predict the remaining useful life (RUL) for reliable services. However, there are three existing challenges in addressing the issue. To overcome these challenges, we introduce M\(^2\)BIST-SPNet for RSEDs RUL prediction. The model incorporates advanced spatiotemporal feature extraction mechanisms, including a spatial-temporal attention-based convolution networks (STACN), multiple branches with multi-scale multifrequency module (MBM), and communication module (CM). Extensive experiments conducted on three typical object datasets (i.e., railway safety relay, EMU contactor, switch circuit controller) shows that the proposed approach has demonstrated state-of-the-art performance in long-term multiparameter prediction and RUL prediction.

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References

  1. Liang H, Zhu L, Yu FR (2023) Collaborative edge intelligence service provision in blockchain empowered urban rail transit systems. IEEE Internet Things J

  2. Liu X, Dabiri A, Wang Y, De Schutter B (2023) Real-time train scheduling with uncertain passenger flows: a scenario-based distributed model predictive control approach. IEEE Trans Intell Transp Syst

  3. Liu X, Dabiri A, Xun J, De Schutter B (2023) Bi-level model predictive control for metro networks: integration of timetables, passenger flows, and train speed profiles. Transp Res E Logist Transp Rev 180:103339

    Article  Google Scholar 

  4. Hu X, Cao Y, Sun Y, Tang T (2022) Railway automatic switch stationary contacts wear detection under few-shot occasions. IEEE Trans Intell Transp Syst 23(9):14893–14907

    Article  Google Scholar 

  5. Zhu L, Chen C, Wang H, Yu FR, Tang T (2023) Machine learning in urban rail transit systems: a survey. IEEE Trans Intell Transp Syst

  6. Hu X, Cao Y, Tang T, Sun Y (2022) Data-driven technology of fault diagnosis in railway point machines: review and challenges. Transp Saf Environ 4(4):036

    Google Scholar 

  7. Liu S, Li Y, Gao S, Xing C, Li J, Cao Y (2023) Prediction of residual electrical life in railway relays based on convolutional neural network bidirectional long short-term memory. Energies 16(17)

  8. Xu Z, Guo Y, Saleh JH (2022) Accurate remaining useful life prediction with uncertainty quantification: a deep learning and nonstationary gaussian process approach. IEEE Trans Reliab 71(1):443–456

    Article  Google Scholar 

  9. Li Q (2022) Rul estimation for rolling bearings using augmented quaternion-based least mean p-power with correntropy induced metric under framework of sparsity. IEEE/ASME Trans Mechatron 1–14

  10. He K, Su Z, Tian X, Yu H, Luo M (2022) Rul prediction of wind turbine gearbox bearings based on self-calibration temporal convolutional network. IEEE Trans Instrum Meas 71:1–12

    Google Scholar 

  11. Zhao Z, Chen F, He X, Lan P, Chen D, Yin X, Yang J (2024) A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system. Appl Energy 357:122478

    Article  Google Scholar 

  12. Du J, Yu FR, Lu G, Wang J, Jiang J, Chu X (2020) MEC-assisted immersive VR video streaming over terahertz wireless networks: a deep reinforcement learning approach. IEEE Internet Things J 7(10):9517–9529

    Article  Google Scholar 

  13. Liu L, Feng J, Wu C, Chen C, Pei Q (2023) Reputation management for consensus mechanism in vehicular edge metaverse. IEEE J Sel Areas Commun

  14. Liang H, Zhu L, Yu FR, Wang X (2022) A cross-layer defense method for blockchain empowered CBTC systems against data tampering attacks. IEEE Trans Intell Transp Syst 24(1):501–515

    Article  Google Scholar 

  15. Feng J, Liu L, Hou X, Pei Q, Wu C (2023) Qoe fairness resource allocation in digital twin-enabled wireless virtual reality systems. IEEE J Sel Areas Commun

  16. Liu J, Ulianov C, Hyde P, Ruscelli AL, Cecchetti G (2022) Novel approach for validation of innovative modules for railway traffic management systems in a virtual environment. Proc Inst Mech Eng F J Rail Rapid Transit 236(2):139–148

    Article  Google Scholar 

  17. Liu J, Chen L, Roberts C, Nicholson G, Ai B (2019) Algorithm and peer-to-peer negotiation strategies for train dispatching problems in railway bottleneck sections. IET Intel Transport Syst 13(11):1717–1725

    Article  Google Scholar 

  18. Zhu L, Shen C, Wang X, Liang H, Wang H, Tang T (2022) A learning based intelligent train regulation method with dynamic prediction for the metro passenger flow. IEEE Trans Intell Transp Syst 24(4):3935–3948

    Article  Google Scholar 

  19. Li Y, Zhu L, Wang H, Yu FR, Tang T, Zhang D (2023) Joint security and resources allocation scheme design in edge intelligence enabled CBTCS: a two-level game theoretic approach. IEEE Trans Intell Transp Syst

  20. Zhang X, Liu P, Lu B, Wang Y, Chen X, Zhang Y, Wang Z (2024) Mtsfbet: a hand-gesture-recognition-based identity authentication approach for passive keyless entry against relay attack. IEEE Trans Mob Comput 23(2):1902–1913. https://doi.org/10.1109/TMC.2023.3243772

    Article  Google Scholar 

  21. Li Y-F, Wang H, Sun M (2023) Chatgpt-like large-scale foundation models for prognostics and health management: a survey and roadmaps. Reliab Eng Syst Saf 109850

  22. Shen P (2016) Research on railway signal relay multi-parameters performance degradation and life prediction. Master’s thesis, Hebei University of Technology

  23. Liu S, Ma C, Liu Y, Li J, Cao Y (2022) Research on railway relay operation state recognition based on improved extreme learning machine. In: He J, Li Y, Yang Q, Liang X (eds) The proceedings of the 16th annual conference of China electrotechnical society. Springer, Singapore, pp 645–653

    Chapter  Google Scholar 

  24. Li W, Ma S, Shen P, Wang N (2017) Study on life prediction of railway relay under vibration. J Electr Eng 12(7):8–15

    Google Scholar 

  25. Sun Y, Cao Y, Zhou M, Wen T, Li P, Roberts C (2019) A hybrid method for life prediction of railway relays based on multi-layer decomposition and rbfnn. IEEE Access 7:44761–44770

    Article  Google Scholar 

  26. Sun Y, Cao Y, Zhang Y, Xu C (2018) A novel life prediction method for railway safety relays using degradation parameters. IEEE Intell Transp Syst Mag 10(3):48–56

    Article  Google Scholar 

  27. Sun Y, Zhang Y, Xu C, Cao Y (2018) Failure mechanisms discrimination and life prediction of safety relay. J Traffic Transp Eng 18(3):138–147

    Google Scholar 

  28. Hu S (2022) Railroad relay composite design technology and performance degradation analysis. Master’s thesis, Hebei University of Technology

  29. Yin S (2021) Research on characteristic parameter fusion method and life prediction analysis of railway relay. Master’s thesis, Hebei University of Technology

  30. Li W, Yin S, Zhao Z, Yanhua X, Pan R (2022) Electromechanical parameters’ fusion method based on adaptive variable weight for railway relay. J Railw Sci Eng 19(1):248–255

    Google Scholar 

  31. Hu X, Tang T, Tan L, Zhang H (2023) Fault detection for point machines: a review, challenges, and perspectives. In: Actuators, vol 12. MDPI, p 391

  32. Hu X, Zhang X, Wang Z, Chen Y, Xia J, Du Y, Li Y (2023) Railway switch machine fault diagnosis considering sensor abnormality scenarios. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp 4834–4839

  33. Chen X, Hu X, Wen T, Cao Y (2023) Vibration signal-based fault diagnosis of railway point machines via double-scale CNN. Chin J Electron 32(5):972–981

    Article  Google Scholar 

  34. Liu J, Wen T, Xie G, Cao Y, Roberts C (2023) Multi-time-scale variational mode decomposition-based robust fault diagnosis of railway point machines under multiple noises. Chin J Electron 33:1–11

    Google Scholar 

  35. Liu J, Wen T, Xie G, Cao Y (2023) Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines. Transp Saf Environ 5(4):065

    Google Scholar 

  36. Zhou F, Bing Kang S, Cohen MF (2014) Time-mapping using space-time saliency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3358–3365

  37. Bandler JW, Cheng QS, Dakroury SA, Mohamed AS, Bakr MH, Madsen K, Sondergaard J (2004) Space mapping: the state of the art. IEEE Trans Microw Theory Tech 52(1):337–361

    Article  Google Scholar 

  38. Shao Z, Zhang Z, Wang F, Wei W, Xu Y (2022) Spatial-temporal identity: a simple yet effective baseline for multivariate time series forecasting. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp 4454–4458

  39. Nie Y, Nguyen NH, Sinthong P, Kalagnanam J (2023) A time series is worth 64 words: long-term forecasting with transformers. In: International Conference on Learning Representations

  40. Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 37, pp 11121–11128

  41. Jin R, Wu M, Wu K, Gao K, Chen Z, Li X (2022) Position encoding based convolutional neural networks for machine remaining useful life prediction. IEEE/CAA J Autom Sin 9(8):1427–1439

    Article  Google Scholar 

  42. Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908–15919

    Google Scholar 

  43. Chen Z, Li X, Wang W, Li Y, Shi L, Li Y (2023) Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network. Reliab Eng Syst Saf 231:108980

    Article  Google Scholar 

  44. Wang S, Fan Y, Jin S, Takyi-Aninakwa P, Fernandez C (2023) Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries. Reliab Eng Syst Saf 230:108920

    Article  Google Scholar 

  45. Jia K, Yu X, Zhang C, Hu W, Zhao D, Xiang J (2023) Software aging prediction for cloud services using a gate recurrent unit neural network model based on time series decomposition. IEEE Trans Emerg Top Comput

  46. Lee Y-S, Tong L-I (2011) Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl-Based Syst 24(1):66–72

    Article  Google Scholar 

  47. Balabin RM, Lomakina EI (2011) Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 136(8):1703–1712

    Article  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, 100044, Beijing, China

    Xiaoxi Hu, Lei Tan & Tao Tang

  2. Intelligent Detection and Monitoring Center, Beijing Municipal Engineering Research Institute, No. 3 Baiwanzhuang, Xicheng District, 100037, Beijing, China

    Lei Tan

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  1. Xiaoxi Hu
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  2. Lei Tan
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  3. Tao Tang
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Correspondence to Xiaoxi Hu.

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Hu, X., Tan, L. & Tang, T. M2BIST-SPNet: RUL prediction for railway signaling electromechanical devices. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06111-y

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