Results 161 to 170 of about 11,244 (210)

An enhanced neural network model for predicting the remaining useful life of proton exchange membrane fuel cells. [PDF]

Sci Rep
Pan H, Zou Y, Sun X, Fu J.
europepmc   +1 more source

Interpretable ensemble remaining useful life prediction enables dynamic maintenance scheduling for aircraft engines. [PDF]

Sci Rep
Özcan H.
europepmc   +1 more source

Prediction of PEMFC life based on IGJO-TCN-BiGRU-Attention. [PDF]

Sci Rep
Zhao J, Shang H, Shen Y, Zhang P, Yang X, Liu P.   +5 more
europepmc   +1 more source

Leveraging transfer learning and maintenance domain adaptation to improve useful life prediction accuracy of high pressure compressor of aeroengine. [PDF]

MethodsX
Singh SNP, Shringi R, Chaturvedi M, Shringi S, Sharma H.   +4 more
europepmc   +1 more source

A deep learning-based prognostic approach for predicting turbofan engine degradation and remaining useful life. [PDF]

Sci Rep
Elsherif SM, Hafiz B, Makhlouf MA, Farouk O.   +3 more
europepmc   +1 more source

A Comprehensive Review of Artificial Intelligence-Based Algorithms for Predicting the Remaining Useful Life of Equipment. [PDF]

Sensors (Basel)
Li W, Chen J, Chen S, Li P, Zhang B, Wang M, Yang M, Wang J, Zhou D, Yun J.   +9 more
europepmc   +1 more source

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KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction

IEEE Transactions on Industrial Electronics, 2022
Machine remaining useful life (RUL) prediction is vital in improving the reliability of industrial systems and reducing maintenance cost. Recently, long short-term memory (LSTM) based algorithms have achieved state-of-the-art performance for RUL prediction due to their strong capability of modeling sequential sensory data.
Qing Xu, Zhenghua Chen, Keyu Wu
exaly   +2 more sources

Prediction of Remaining Useful Life (RUL) of Bearing Using Exponential Degradation Model

Lecture Notes in Mechanical Engineering, 2022
Keval Bhavsar, Vinay Vakharia
exaly   +2 more sources