Long Short-Term Memory Network for Remaining Useful Life estimation

By Shuai Zheng, Kosta Ristovski, Ahmed K. Farahat, Chetan Gupta. International Conference on Prognostics and Health Management, 2017

759 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Medical Laboratory Technology, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/e66afb33d246dbe3199fd57bcfc1b611136d96c2

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. Rui Zhao
2. Ruqiang Yan
3. Min Wu
4. Vilmar Æsøy
5. Satish Nagarajaiah
6. Sergey Ushakov
7. Paulo da Costa
8. Chau Yuen

In Safety, Risk, Reliability and Quality

1. Daoquan Chen
2. Xiuze Zhou
3. Wei-Cheng Hong

Citations in the corpus

• Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, positive): “…types of layers can be stacked above RNN layers to perform special functions, such as capturing intrinsic features with autoencoder for better performance in early fault detection (Lu et al. 2018), and denoising with convolutional layers when processing high-frequency data (Zheng et al. 2017).”
• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “Zheng et al. [12] showed that a sequence of LSTM layers followed by FFNNs could outperform other methods including CNN’s in three distinct degradation datasets.”
• Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “achieved even higher accuracy on the C-MAPSS dataset compared to both the LSTM approach in [6] and the CNN approach in [5].”
• A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, neutral): “[39].”
• Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2022, positive): “To ensure safety, prediction of RUL in advance provides some key information about the maintenance and replacement of batteries [8]–[10].”
• Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery (2018, methodology): “In this paper, we use the piecewise linear degradation model [1] depicted in Fig.”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, methodology): “Zheng et al. [21] presented a long short-term memory (LSTM)-based RUL prediction.”