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

By Yuting Wu, Mei Yuan, Shaopeng Dong, Li Lin, Yingqi Liu. Neurocomputing, 2018

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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, result): “This was precisely demonstrated in the study by Wu et al., 258 where the LSTM approach for RUL of engineered systems was proposed.”
Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, methodology): “Recurrent neural networks (Hopfield, 1982; Rumelhart et al., 1988; Graves et al., 2013; Graves, 2013, especially LSTM (Gers, 1999; Graves and Schmidhuber, 2005) networks have been widely used for PHM applications (Yuan et al., 2016; Malhotra et al., 2016; Zhao et al., 2016, 2017; Wu et al., 2018).”
Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, background): “…proposed to prognostics problems containing high amounts of time-series input data, including Convolutional Neural Networks (CNN) [10] and variations of RNNs capable of dealing with long input sequences such as the Long Short-Term Memory Networks (LSTM)[11, 12] and Gated Recurrent Unit (GRU) [13].”