Research on Remaining Useful Life Predictive Model of Machine Parts Based on SVM and Kalman Filter

doi:10.3969/j.issn.1000-1093.2018.05.020

Abstract

Abstract: A new remaining useful life (RUL) predictive model for machine parts is proposed, which combines support vector machine (SVM) and non-linear Kalman filter. The proposed model is expected to fix the awkward situation in that the degradation data from both database and the predicted part cannot be used at the same time by most of the exiting RUL models. The SVM regression model trained by data from full-life tests is treated as the status update equation of non-linear Kalman filter. The time update equation is constructed according to the degradation characteristics of machine parts. RUL estimations and corresponding confidence intervals of point-in-time are computed iteratively after setting the initial RUL value and its variance. The proposed model makes the best use of data from both full-life tests of the same or similar parts and current part during its degradation. The accuracy of RUL estimation, the stability and practical values of the proposed model are illustrated by analyzing a certain type of antifriction bearings. Key

Key words: machinepart, remainingusefullife, supportvectormachine, non-linearKalmanfilter, confidenceinterval

CLC Number:

TB114.37

YU Zhen-liang, SUN Zhi-li, CAO Ru-nan, WANG Peng. Research on Remaining Useful Life Predictive Model of Machine Parts Based on SVM and Kalman Filter[J]. Acta Armamentarii, 2018, 39(5): 991-997.

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第39卷
第5期2018 年5月兵工学报ACTA
ARMAMENTARIIVol.39No.5May2018

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