Abstract
Failure prognosis is the key point of prognostic and health management or condition-based maintenance, the multiple uncertainty sources in real world will lead to inaccurate prediction. In this paper, an advanced failure prognosis method with Kalman filter is presented to address the real-world uncertainties. The multiple uncertainty sources are analyzed and classified first and then theoretical methods are derived, respectively, for the different uncertainty sources. Afterward, the failure prognosis algorithm is developed by taking into consideration. In the end, an aircraft fuel feeding system health monitoring case simulation is presented to demonstrate the effectiveness of the proposed method.
Highlights
Various failure prognosis methods based on measured data or degradation information can be used to evaluate system’s degradation trend and estimate the remaining useful life
Failure prognosis methods can be seen as the bridge between the front-end field sensor data acquisition and back-end fusion/inference machine and the critical component of prognostic and health management (PHM) or condition-based maintenance (CBM)
A failure prognosis approach with multiple uncertainty sources has been presented. Such approach utilizes adaptive updating scheme, robust filter, and total probability formula to form the advanced failure prognosis tool which can predict the residual life based on the measured data
Summary
Various failure prognosis methods based on measured data or degradation information can be used to evaluate system’s degradation trend and estimate the remaining useful life. If the system noise is assumed to be zero, and no new information is measured in equation [4], system state in future can be calculated by equation [5] in the probability sense For system state estimation and prediction based on stochastic filter, the system’s model is completely unknown Such error can be seen as system model parameter uncertainty. Based on measurement output error w^k in kth step, the deviation estimation process presented in equations [14]–(16) can be used to update system matrix’s uncertainty item DAk = EkDkFk with dk, which realizes system parameter’s adaptive updating. The filter with filtering gain K is robust if there exist real constants a, b, and g and the gain which satisfy the following linear matrix inequality (LMIs)
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