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Fault Tolerance in Electric Vehicles Using Deep Learning for Intelligent Transportation Systems

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Abstract

Intelligent transportation systems (ITS) such as hybrid electric vehicles make use of sensing technologies to improve mobility, safety and efficiency. Automated manual transmission (AMT) is a mechatronic device consisting of Internet of Things (IoT)-enabled sensors and actuators responsible for automatic gear shifting in hybrid electric vehicles. Any failure in these sensors or actuators can affect the normal operation of vehicles. Therefore, this study aims to discuss the characteristics of AMT when it breaks down and its impact on the whole hybrid electric vehicle system. Firstly, this paper briefly introduces the relevant overview of hybrid electric vehicles and AMT. Next, analytical redundancy analysis is used to find out the possible faults of each part of AMT and the causes of each fault. The normal and faulty signals are then passed to a reduced depth kernel extreme learning machine (RDK-ELM) algorithm, which combines the deep learning network structure with the kernel-based selection of support vectors from the training samples. The RDK-ELM is a fault diagnosis algorithm that classifies the normal and faulty signals, which represent whether the sensor is faulty or not. Simulation results show that the algorithm has high classification accuracy of 97.12% and it requires less time for training the model.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Sanguesa JA, Torres-Sanz V, Garrido P, Martinez FJ, Marquez-Barja JM (2021) “A Review on Electric Vehicles: Technologies and Challenges,” Smart Cities, vol. 4, no. 1, Art. no. 1, Mar. https://doi.org/10.3390/smartcities4010022

  2. Yang C, Zha M, Wang W, Liu K, Xiang C (2020) Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: review and recent advances under intelligent transportation system. IET Intel Transport Syst 14(7):702–711. https://doi.org/10.1049/iet-its.2019.0606

    Article  Google Scholar 

  3. Qin G, Ge A, Li H (2004) “On-Board Fault Diagnosis of Automated Manual Transmission Control System,” IEEE Transactions on Control Systems Technology, vol. 12, no. 4, pp. 564–568, Jul. https://doi.org/10.1109/TCST.2004.825133

  4. Hofman T, Ebbesen S, Guzzella L (Jul. 2012) Topology optimization for Hybrid Electric Vehicles with Automated Transmissions. IEEE Trans Veh Technol 61(6):2442–2451. https://doi.org/10.1109/TVT.2012.2196299

  5. Frank PM (May 1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results. Automatica 26(3):459–474. https://doi.org/10.1016/0005-1098(90)90018-D

  6. Huang G-B, Zhu Q-Y, Siew C-K (2006) “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489–501, Dec. https://doi.org/10.1016/j.neucom.2005.12.126

  7. Wei S, Ma N, Su J, Deng W, “A Motor Fault Detection Method Based on Optimized Extreme Learning Machine (2021),” in Advances in Artificial Systems for Medicine and Education IV, Z. Hu, S. Petoukhov, and M. He, Eds., in Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, pp. 315–324. https://doi.org/10.1007/978-3-030-67133-4_29

  8. Suthar V, Vakharia V, Patel VK, Shah M (2023) “Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine,” Machines, vol. 11, no. 1, Art. no. 1, Jan. https://doi.org/10.3390/machines11010029

  9. Lim J-Y, Ji P-S (2016) Development of Fault diagnosis algorithm using correlation analysis and ELM. Trans Korean Inst Electr Eng P 65(3):204–209. https://doi.org/10.5370/KIEEP.2016.65.3.204

    Article  Google Scholar 

  10. Lu F, Jiang J, Huang J, Qiu X (Dec. 2017) Dual reduced kernel extreme learning machine for aero-engine fault diagnosis. Aerosp Sci Technol 71:742–750. https://doi.org/10.1016/j.ast.2017.10.024

  11. Liang R, Chen Y, Zhu R (2022) “A Novel Fault Diagnosis Method Based on the KELM Optimized by Whale Optimization Algorithm,” Machines, vol. 10, no. 2, Art. no. 2, Feb. https://doi.org/10.3390/machines10020093

  12. Li B, Rong X, Li Y (2014) “An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures,” The Scientific World Journal, vol. p. e906546, Apr. 2014, https://doi.org/10.1155/2014/906546

  13. Avci D (2016) An automatic diagnosis system for Hepatitis Diseases based on genetic Wavelet Kernel Extreme Learning Machine. J Electr Eng Technol 11(4):993–1002. https://doi.org/10.5370/JEET.2016.11.4.993

    Article  Google Scholar 

  14. Xia J, Yang D, Zhou H, Chen Y, Zhang H, Liu T, Heidari AA, Pan Z (Feb. 2022) Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Comput Biol Med 141:105137. https://doi.org/10.1016/j.compbiomed.2021.105137

  15. Liu Z, Hao J, Yang D, Tahir GA, Pan M (2022) “A Novel Reformed Reduced Kernel Extreme Learning Machine with RELIEF-F for Classification,” Computational Intelligence and Neuroscience, vol. p. e4795535, Mar. 2022, https://doi.org/10.1155/2022/4795535

  16. Deng W-Y, Ong Y-S, Zheng Q-H (Apr. 2016) A fast reduced Kernel Extreme Learning Machine. Neural Netw 76:29–38. https://doi.org/10.1016/j.neunet.2015.10.006

  17. Hinton GE, Osindero S, Teh Y-W (2006) “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, Jul. https://doi.org/10.1162/neco.2006.18.7.1527

  18. Tamilselvan P, Wang P (2013) Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf 115:124–135. https://doi.org/10.1016/j.ress.2013.02.022

    Article  Google Scholar 

  19. Usman M, Jan MA, Jolfaei A (2020) SPEED: a deep learning assisted privacy-preserved framework for intelligent transportation systems. IEEE Trans Intell Transp Syst 22(7):4376–4384

    Article  Google Scholar 

  20. Beard RV (1971) “Failure accomodation in linear systems through self-reorganization.,” Thesis, Massachusetts Institute of Technology, Accessed: May 16, 2023. [Online]. Available: https://dspace.mit.edu/handle/1721.1/16415

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Authors and Affiliations

  1. Department of Automobile Engineering, Hebei Petroleum University of Technology, Chengde, 067000, China

    Huanxue Liu, Fengqin Ke, Zhenzhong Zhang, Yanan Gao & Quanyu Zhang

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  1. Huanxue Liu
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  2. Fengqin Ke
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  4. Yanan Gao
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Correspondence to Fengqin Ke.

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Liu, H., Ke, F., Zhang, Z. et al. Fault Tolerance in Electric Vehicles Using Deep Learning for Intelligent Transportation Systems. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02168-w

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