Real-time and accurate residual life prediction for an inertial measurement unit (IMU) can provide effective decision support for timely and cost-effective maintenance scheduling. The performance index reflecting the degradation of the IMU cannot be observed directly and direct measurements are contaminated by noise. Thus, a state space model was developed to predict the residual life of an IMU. Since the changes in the degradation state of the IMU are nonlinear over time, this analysis was a nonlinear drift-driven Brownian motion (BM) is used to characterize the degradation process, with the expectation maximization (EM) algorithm and the Kalman filter used to jointly estimate and update the state and model parameters. Furthermore, the estimated state distribution is incorporated into the residual life model using an approximate analytical form of the distribution. The approach is validated by comparison with experimental data which indicates that this method gives better prediction accuracies and lower uncertainties.
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Lei FENG,Hongli WANG,Zhijie ZHOU,Xiaosheng SI,Hongxing ZOU. Residual life prediction based on the state space for inertial measurement units. Journal of Tsinghua University(Science and Technology), 2014, 54(4): 508-514.
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