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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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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