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Distribution Free Prediction Interval For Uncertainty Quantification In Remaining Useful Life Prediction, Huimin Chen
Distribution Free Prediction Interval For Uncertainty Quantification In Remaining Useful Life Prediction, Huimin Chen
Huimin Chen
Remaining useful life (RUL) prediction is an important component for system health monitoring and prognosis. Ideally, one expects the prediction algorithm to provide the complete distribution of the RUL prediction over time taking various uncertainties into account. However, the dynamic model being used to characterize state estimation and future loading uncertainties is often simplified through various approximations, leading to non-credible predicted distribution. Nevertheless, certain algorithm may only provide a point estimate of the RUL, making it difficult to quantify the uncertainty of the prediction. In this paper, we focus on interval prediction with high probability that guarantees finite sample validity …
On Optimizing Decision Fusion With A Budget Constraint, Huimin Chen, Vesseline P. Jilkov, X. Rong Li
On Optimizing Decision Fusion With A Budget Constraint, Huimin Chen, Vesseline P. Jilkov, X. Rong Li
Huimin Chen
We consider the problem of fusing local decision outputs into a global decision with a budget constraint. Each local decision maker is assumed to provide finite output regarding two competing hypotheses. A fusion rule is characterized by probabilistic mixing of decision trees corresponding to deterministic policies to reach a global decision. For practical problems where maximizing detection probability is of primary concern, we propose to optimize the fusion rule under the budget constraint via dynamic programming. The proposed algorithm can construct the complete efficient front of the detection probability vs. cost for practical decision fusion problems. Illustrative examples regarding the …