Integrating Advanced Prognostic Methods for Accurate Remaining Useful Life Prediction in Industrial Systems
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Abstract
Accurate remaining useful life (RUL) prediction of industrial system is critical to ensure smooth operation and its safety. Various prognostic methods have been developed but there still exist critical challenges for field applications. One challenge is the unhealth degradation exhibiting the change of state from those of normal degradation. Another is the prediction in the face of severe noise with limited data (i.e., early prediction) using empirical models. Final challenge is the prediction under varying operating conditions, which occurs in practice in various industrial applications. To overcome these challenges, this research proposes advanced prognostics methods with different recipes featured by high adaptability, physical constraints, and monotonic health indicator (HI). The developed methods are validated with specific case studies involved with the challenges.
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prognostics, remaining useful life, Bayesian inference, state change, low-fidelity physical information, time-varying operating conditions
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