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Residual life predictions from vibration-based degradation signals: A neural network approach

机译：基于振动的退化信号的残余寿命预测：神经网络方法

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Nagi Gebraeel; Mark Lawley; R. Liu; Vijay Parmeshwaran;
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年度 2014
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1. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods [J] . Runqing Huang, Lifeng Xi, Xinglin Li, Mechanical systems and signal processing . 2007,第1期

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2. Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning [J] . Hao Zhang, Qiang Zhang, Siyu Shao, Shock and vibration . 2020,第1期

机译：顺序网络具有剩余机器旋转机器的剩余寿命预测使用深度转移学习
3. Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction [J] . C. Sbarufatti, M. Corbetta, A. Manes, International Journal of Fatigue . 2016,第FEBaPTa1期

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4. Real-time sequential Monte Carlo sampling based on a committee of artificial neural networks for residual lifetime prediction of a component subjected to fatigue crack growth [C] . Claudio Sbarufatti, Matteo Corbetta, Andrea Manes International Colloquium on Mechanical Fatigue of Metal . 2014

机译：基于人工神经网络委员会的实时顺序蒙特卡罗抽样，用于疲劳裂纹生长的组分的残余寿命预测
5. Real-time degradation modeling and residual life prediction for component maintenance and replacement [D] . Gebraeel, Nagi 2003

机译：实时退化建模和剩余寿命预测，用于组件维护和更换
6. Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals [O] . Francesco Di Nardo, Christian Morbidoni, Guido Mascia, 2020

机译：通过神经网络解释EMG信号的步态事件预测的主题方法
7. Residual Life Distributions from Component Degradation Signals: A Bayesian Approach [O] . Gebraeel, Nagi, Lawley, Mark, LI, Rong, 2005

机译：组件降解信号的剩余寿命分布：贝叶斯方法

Residual life predictions from vibration-based degradation signals: A neural network approach

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