Bearing Remaining Useful Life Estimation Using Proximal Policy Optimization (PPO): Validation on the XJTU-SY Run-to-Failure Dataset

This study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings. Although DRL has shown growing promise in prognostics, existing applicat...

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Principais autores:	Shahil Kumar, Giansalvo Cirrincione, Rahul Ranjeev Kumar
格式:	Artigo
語言:	Inglês
出版:	MDPI AG 2026-06-01
叢編:	Machines
主題:	bearings long short term memory multi-layer perceptron proximal policy optimization remaining useful life
在線閱讀:	https://www.mdpi.com/2075-1702/14/6/672
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