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Prediction and Abnormality Assertion on Emu Brake Pad Based on Multivariate Integrated Random Walk

Hongsheng Su1,2,∗, Shuangshuang Wang1, Dengfei Wang2

1 School of Automation and Electrical Engineering, Lanzhou Jiaotong University,Lanzhou, China
2 Key Laboratory for the Highland Traffic Information Engineering and Control in Gansu province, Lanzhou 730070, China

* Corresponding Author: E-mail: email

Computer Systems Science and Engineering 2018, 33(5), 351-360.


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.


Cite This Article

H. Su, S. Wang and D. Wang, "Prediction and abnormality assertion on emu brake pad based on multivariate integrated random walk," Computer Systems Science and Engineering, vol. 33, no.5, pp. 351–360, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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