Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network

By Mei Yuan, Yuting Wu, Li Lin. AUS, 2016

347 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ecbb83c2ce28d05e098c8c36bb26c7e4425bd039

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Olga Fink
2. Paulo da Costa
3. Uzay Kaymak
4. Yingqian Zhang
5. Alp Eren Akçay
6. Xiang Li
7. Qian Ding
8. Zhen Hu

In Electrical and Electronic Engineering

1. Zhen Hu
2. Jianqiao Sun
3. Qian Ding
4. Ali Lalbakhsh
5. Xiang Li
6. Byeng Dong Youn
7. Chao Hu

Citations in the corpus

• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “Most previous studies have focused on predicting the RUL when enough run-to-failure data is available and assuming the training and future data come from the same distribution and feature space [10, 11, 12, 32, 29].”
• 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).”
• Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “Reference [43] shows fault prediction to be the main subject in nonlinear systems [44].”
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, background): “However, it can be further optimized to get better results [22,47].”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “, 2018, 2019a), and LSTM recurrent neural networks (Yuan et al., 2016; Huang et al., 2019).”
• Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “proposed an LSTM approach for several different faults [17].”