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Hybrid remaining useful life prediction method. A case study on railway D-cables

机译：混合剩余的有用寿命预测方法。 铁路D-电缆的案例研究

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摘要

This paper develops a hybrid remaining useful life (RUL) prediction method and explores the feasibility for complex system equipment, using one of transmission equipment D-cables in high-speed railways as an example. RUL prediction is a promising way to reduce high maintenance costs for high-speed railways. However, there is no sufficient actual life-cycle data due to the lack of sensors, and no mature physics-of-failure model of the equipment in high-speed railways, which make it difficult to predict RUL. To solving this problem, firstly the failure modes, mechanisms, and effects of the D-cables are first analyzed, and accelerated life tests are run under different thermal stresses in Ansys to obtain the life-cycle data. Based on the life-cycle data, particle filtering (PF) method predicts the RUL based on Paris-Law model, meanwhile feedforward neural network (FNN) predicts the RUL under the same thermal stress with PF method, finally a hybrid RUL prediction method that combines model-based and data-driven methods is developed. The results are verified using simulation.
机译：本文开发了一种混合剩余的使用寿命（RUL）预测方法，并利用在高速铁路中的传输设备D-电缆之一作为示例来探讨复杂系统设备的可行性。 RUL预测是降低高速铁路的高维护成本的有希望的方式。然而，由于缺乏传感器，没有足够的实际生命周期数据，高速铁路中的设备没有成熟物理 - 失效模型，这使得难以预测rul。为了解决这个问题，首先是首先分析D-电缆的故障模式，机构和效果，并且在ANSYS中的不同热应力下运行加速寿命测试以获得生命周期数据。基于生命周期数据，粒子滤波（PF）方法预测基于巴黎法模型的RUL，同时前馈神经网络（FNN）预测RUL在与PF方法相同的热应力下的RUL，最后是一种混合RUL预测方法结合了基于模型和数据驱动的方法。使用模拟验证结果。

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