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Keywords: Prognostics and health management (PHM) Data-driven prognostics Remaining useful life COPULA Reliability a b s t r a c t This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a(More)
1. Abstract This paper presents a maximum confidence enhancement based sequential sampling approach for simulation-based design under uncertainty. In the proposed approach, the ordinary Kriging method is adopted to construct surrogate models for all constraints and thus Monte Carlo simulation (MCS) is able to be used to estimate reliability and its(More)
The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust,(More)
Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor(More)
Advances in high-performance sensing technologies enable the development of wind turbine condition monitoring systems to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two-stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an(More)