Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

(2018) Mohamed Elforjani et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Strength of SiC f -SiC m composite tube under uniaxial and multiaxial loading

(2018) Kirill Shapovalov et al.   JOURNAL OF NUCLEAR MATERIALS

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

(2018) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

(2018) Yuting Wu et al.   NEUROCOMPUTING

Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

(2018) Ansi Zhang et al.   Applied Sciences-Basel

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

(2017) Dazhong Wu et al.   JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME

Multi-bearing remaining useful life collaborative prediction: A deep learning approach

(2017) Lei Ren et al.   JOURNAL OF MANUFACTURING SYSTEMS

In-situ damage monitoring of a SiC/SiC ceramic matrix composite using acoustic emission and digital image correlation

(2016) Travis Whitlow et al.   COMPOSITE STRUCTURES

Experimental study of thermo-mechanical behavior of SiC composite tubing under high temperature gradient using solid surrogate

(2015) Luis Alva et al.   JOURNAL OF NUCLEAR MATERIALS

Deep learning

(2015) Yann LeCun et al.   NATURE

Mechanical properties and real-time damage evaluations of environmental barrier coated SiC/SiC CMCs subjected to tensile loading under thermal gradients

(2015) M.P. Appleby et al.   SURFACE & COATINGS TECHNOLOGY

Silicon carbide composites as fusion power reactor structural materials

(2011) L.L. Snead et al.   JOURNAL OF NUCLEAR MATERIALS