首页|Study Data from University of Franche-Comte Update Knowledge of Machine Learning (Explainable Rul Estimation of Turbofan Engines Based On Prognostic Indicators and Heterogeneous Ensemble Machine Learning Predictors)
Study Data from University of Franche-Comte Update Knowledge of Machine Learning (Explainable Rul Estimation of Turbofan Engines Based On Prognostic Indicators and Heterogeneous Ensemble Machine Learning Predictors)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Besancon, France, by NewsRx correspondents, research stated, “Data -driven prognostics of systems ex ploit sensor measurements to predict the degradation evolution and anticipate fa ilures, corresponding to the estimation of the remaining useful life (RUL). This task uses feature engineering to build prognostic indicators (HI) and machine l earning (ML) to estimate the RUL.” Our news journalists obtained a quote from the research from the University of F ranche-Comte, “However, high variability in data coming from similar systems ope rating under different conditions negatively affects the RUL performance. Hence, this paper presents a new methodology that combines feature and ML engineering methods to provide an explainable RUL prediction. The key contributions lie in c onstructing efficient prognostic indicators that isolate distinct profile trajec tories, enabling adaptive RUL extraction for each system. An ensemble of heterog eneous ML predictors is also trained using these indicators and RUL trajectories , effectively addressing variability issues and enhancing RUL performance.”
BesanconFranceEuropeCyborgsEmerg ing TechnologiesEngineeringMachine LearningUniversity of Franche-Comte
NETL
NSTL
