基于多元积分随机游走的动车组刹车片异常预测与断言

H. Su, Shuangshuang Wang, Dengfei Wang
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摘要

为了更好地解决电动单元(EMU)刹车片在不断变化的运行环境中由于各种随机因素导致的异常失效问题,本文提出了一种基于多元积分随机游走(MIRW)模型的性能预测与异常决策评价方法。该方法首先建立了动车组刹车片性能退化的状态空间模型。然后根据实测数据,采用固定间隔前向-后向平滑算法提取刹车片性能退化趋势。在此基础上,用卡尔曼预测器对未来的退化状态进行预测。根据得到的状态估定值,应用Grubbs准则判断刹车片在达到最终失效前是否处于异常状态,是否在出现新样本时处于异常状态,从而确定异常失效容限范围(AFTR)。此外,还估算了刹车片在退化过程中的累积失效概率。最后,将某型动车组刹车片的厚度数据应用于未来退化状态的预测和异常状态的判断,结果表明该方法具有更高的效率和准确性。
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Prediction and Abnormality Assertion on Emu Brake Pad Based on Multivariate Integrated Random Walk
To better solve the issue with abnormal failure of electric motor unit (EMU) brake pad resulted from various random factors in the ever-changing operating environment, in this paper, a new evaluation method of performance prediction and abnormity decision is proposed based on the Multivariate integrated random walk (MIRW) model. In this method, the state space model of the EMU brake pad performance degradation is firstly established. And then based on the observed data, the brake pad performance degradation trend is extracted by the fixed interval forward - backward smoothing algorithm. Based on it, the future degradation state can be predicted by Kalman predictor. Based on the obtained state estimation values, abnormal failure tolerance range (AFTR) can be determined applying Grubbs criterion to judge whether the brake pad is being in abnormal state before reaching the final failure or not as a new sample appears. In addition, the cumulative failure probability of the brake pad is estimated in the degradation process. Finally, the thickness data of a certain type of EMU brake pad is applied to predict the future degradation state and determine the abnormal condition, and the result shows that the proposed method is more efficient and accurate.
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