A Model-Based Method for Remaining Useful Life Prediction of Machinery

By Yaguo Lei, Naipeng Li, S. Gontarz, Jing Lin, S. Radkowski, J. Dybała. IEEE Transactions on Reliability, 2016

526 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/b6d328d9d0eeab6e4a3e49f53b6e1d76c4cf8c61

How researchers see this paper

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

In Civil and Structural Engineering

1. Enrico Zio
2. Yaguo Lei
3. Naipeng Li
4. Boyuan Yang
5. Ruonan Liu

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Enrico Zio
5. Liang Guo
6. Tao Yan
7. Boyuan Yang
8. Ruonan Liu

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Ningbo Li
4. Liang Guo
5. Enrico Zio
6. Tao Yan
7. Jing Lin
8. Boyuan Yang

Citations in the corpus

• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[56] transformed the PE model into an empirical model for RUL prediction of machinery.”
• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, methodology): “…take advantage of prior knowledge on the degradation and failure mechanisms to model the degradation and failure behavior of an equipment mathematically [6], e.g., Paris–Erdogan model [7] and exponential model [8] for crack propagation with particle filtering used as parameter estimator [9].”