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Found 3 papers, 3 papers with code
A physics-informed neural network for wind turbine main bearing fatigue
Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss).
Physics-informed neural networks for corrosion-fatigue prognosis
The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion).
Fleet Prognosis with Physics-informed Recurrent Neural Networks
The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.
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