Abstract
The power supply in rail transit signal system is the major power source of the devices. During service, the temperature of power supply changes dynamically, which influence its degradation. The degradation of power supply will directly affect the function of other devices in system. Therefore, it is significant to evaluate the remaining useful life of power supply under variable temperature to ensuring the operation reliability of signal system. The particle filter method has been widely used for the life prediction of industrial products, as it can solve the life prediction problem of nonlinear system. Nevertheless, the predicted effect of this method is highly depended on the state of particles. For the power supply with sudden degradation, the particle impoverishment problem is happened, which cause a significant predicted variance. To solve the problem, this paper proposed an enhancement particle filter life prediction method. The effectiveness of the proposed method was evaluated using the mutated ripple voltage of power supply under variable temperature conditions. The results indicate that this method can sufficiently reduce the estimation variance by identifying the region where the degradation is evident, as well as by sustaining particle diversity. Based on this method, the precise life prediction results of the power supply can be acquired.
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1. References
Pindado, S., Alcala, G.D., Alfonso, C.: Improving the power supply performance in rural smart grids with photovoltaic DG by optimizing fuse selection. Agronomy 11(4), 622–629 (2021)
Lafoz, M., Navarro, G., Torres, J.: Power supply solution for ultrahigh speed hyperloop trains. Smart Cities 3(3), 642–656 (2020)
Efthimiou, N., Loudos, G., Karakatsanis, N.A.: Effect of 176Lu intrinsic radioactivity on dual head PET system imaging and data acquisition, simulation, and experimental measurements. Med. Phys. 40(11), 109–125 (2013)
Raheem, A.: Optimal design of multiple constant-stress accelerated life testing for the extension of the exponential distribution under type-II censoring. J. Comput. Appl. Math. 38(2), 104–113 (2020)
Ishaque, K., Salam, Z., Syafaruddin, H.: A comprehensive MATLAB Simulink PV system simulator with partial shading capability based on two-diode model. Sol. Energy 85(9), 2217–2227 (2011)
Liu, D.T., Zhou, J.B., Peng, Y.: Data-driven prognostics and remaining useful life estimation for lithium-ion battery: a review. Instrumentation 1(35), 61–72 (2014)
Sanchez, H.E., Sankara, S., Escobet, T., Puig, V., Kai, G.: Analysis of two modeling approaches for fatigue estimation and remaining useful life predictions of wind turbine blades. In: Third European Conference of the Prognostics and Health Management Society, pp. 14–21. Bilbao, Spain (2016)
Marble, S., Morton, B.P.: Predicting the remaining life of propulsion system bearings. In: IEEE Aerospace Conference, pp. 1–8. MT, USA (2006)
Kulkarni, C.S., Biswas, G., Jose, R.C.: Physics based degradation models for capacitor prognostics under thermal overstress conditions. Int. J. Prognostics Health Manage. 4(5), 12–19 (2013)
Zio, E., Maio, F.D.: A Data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear power plant. Reliab. Eng. Syst. Saf. 95(1), 49–57 (2010)
Sun, Y., Cao, Y., Zhang, Y.: A novel life prediction method for railway safety relays using degradation parameters. IEEE Intell. Transp. Syst. Mag. 43(7), 118–126 (2018)
Ding, Z., Zhou, Y., Pu, G., Zhou, M.C.: Online failure prediction for railway transportation systems based on fuzzy remaining useful life and data analysis. IEEE Trans. Reliab. 26(9), 1–16 (2018)
Baek, S.Y., Dong, H.B.: A study on the fatigue life prediction of the various gas-welded joints using a probabilistic statistics technique. Met. Mater. Int. 17(1), 143–149 (2011)
Compare, M., Baraldi, P., Turati, P.: Interacting multiple-models, state augmented particle filtering for fault diagnostics. Probab. Eng. Mech. 40(6), 12–24 (2015)
Orchard, M.E., Vachtsevanos, G.J.: A particle filtering approach for on-line fault diagnosis and failure prognosis. Trans. Inst. Meas. Control. 31(3–4), 221–246 (2007)
Pang, H.M., Bryan, P.: A life prediction scheme for electrolytic capacitors in power converters without current sensor. In: Applied Power Electronics Conference & Exposition, PP. 973–979. Palm Springs, USA (2010)
Celaya, J.R., Kulkarni, C, Saha, S.: Accelerated aging in electrolytic capacitors for prognostics. In: Proceedings Annual Reliability and Maintainability Symposium, pp. 1–6. USA (2012)
Zhang, N., Xu, A., Wang, K.: Remaining useful life prediction of lithium batteries based on extended kalman particle filter. IEEE Trans. Electr. Electr. Eng. 16(2), 206–214 (2021)
Acknowledgment
This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1600705, in part by the Beijing Science and Technology Project under Grant Z191100002519003, and in part by the Traffic Control Technology Co., Ltd.
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Zhao, J. et al. (2021). A Life Prediction Method for Power Supply in Rail Transit Signal System Under Variable Temperature Conditions. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_1
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DOI: https://doi.org/10.1007/978-981-16-7502-7_1
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