Searched for: subject%3A%22Maintenance%255C%252Bplans%22
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document
Mitici, M.A. (author), de Pater, I.I. (author), Zeng, Zhiguo (author), Barros, Anne (author)
We pose the maintenance planning for systems using probabilistic Remaining Useful Life (RUL) prognostics as a renewal reward process. Data-driven probabilistic RUL prognostics are obtained using a Convolutional Neural Network with Monte Carlo dropout. The maintenance planning model is illustrated for aircraft turbofan engines. The results show...
conference paper 2023
document
Mitici, M.A. (author), de Pater, I.I. (author), Barros, Anne (author), Zeng, Zhiguo (author)
The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the...
journal article 2023