RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model

By Qingqing Zhai, Z. Ye. IEEE Transactions on Industrial Informatics, 2017

313 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/6c1c75a023d34f4f2ccc6433bdda04ab00cd403a

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. Nan Chen
2. Qin Wang
3. Weiwen Peng
4. Jun Zhu
5. Olga Fink
6. Markus Svensén
7. Pierre Dersin
8. Mélanie Ducoffe

In Safety, Risk, Reliability and Quality

1. Nan Chen
2. Qin Wang
3. Olga Fink
4. Markus Svensén
5. Pierre Dersin
6. Mélanie Ducoffe
7. Wan-Jui Lee
8. Zhu Mao

Citations in the corpus

• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, unknown): “of mechanical systems (or components) [2], [3].”
• Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, background): “However, how to effectively extract features from data and accurately predict RUL is still considered as a challenge for PHM [3]–[7].”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, methodology): “ RUL prediction consists of modelling degradation trajectories by stochastic processes that capture more or less accurately degradation physics. An example of that approach is given by Zhai and Ye in [14]. One of the research directions is also on assessing the performance of prognostics algorithms ”