A multi-time scale approach to remaining useful life prediction in rolling bearing

By Yuning Qian, Ruqiang Yan, Robert X. Gao. 2017

216 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/06a48ded200a8d0662bfb7963d42250483c29b7b

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. Xiang Li
2. Qian Ding
3. Jianqiao Sun
4. Jing Lin
5. Kaixuan Liang
6. Jinyang Jiao
7. Ming Zhao
8. Dhiraj Neupane

In Mechanical Engineering

1. Xiang Li
2. Qian Ding
3. Jianqiao Sun
4. Dhiraj Neupane
5. Jong-Hoon Seok
6. Jing Lin
7. Kaixuan Liang
8. Jinyang Jiao

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].”
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, background): “While model-based approaches tend to be more accurate if the complex system degradation is modeled precisely [7], they require extensive prior knowledge about physical systems which is usually unavailable in practice, e.g. aircraft engines.”