Failure prognosis is the key point of prognostic and health management or condition-based maintenance, the multiple\nuncertainty sources in real world will lead to inaccurate prediction. In this paper, an advanced failure prognosis\nmethod with Kalman filter is presented to address the real-world uncertainties. The multiple uncertainty sources are\nanalyzed and classified first and then theoretical methods are derived, respectively, for the different uncertainty\nsources. Afterward, the failure prognosis algorithm is developed by taking into consideration. In the end, an aircraft\nfuel feeding system health monitoring case simulation is presented to demonstrate the effectiveness of the proposed\nmethod.
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