RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network

Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challe...

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Autori principali:	Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-02-01
Serie:	Lubricants
Soggetti:	remaining useful life prediction rolling bearing FOA CNN LSTM
Accesso online:	https://www.mdpi.com/2075-4442/13/2/81
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