Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

By Chao Hu, Byeng Dong Youn, Pingfeng Wang, Joung Taek Yoon. IEEE Conference on Prognostics and Health Management, 2011

367 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ffd8112eae611c8a0b05699bfb3fd8d0e84dab5b

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. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Zhen Hu
8. A. K. Qin

In Electrical and Electronic Engineering

1. Zhen Hu
2. Robert X. Gao
3. David A. Dornfeld
4. Soundar Kumara *
5. Roberto Teti
6. Xiaoping Du
7. Lihui Wang

Citations in the corpus

• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, methodology): “Traditionally, clustering was applied directly to the preprocessed condition monitoring data or the handcrafted features (Detroja et al., 2006; Hu et al., 2012; Li and Zhou, 2014; Du et al., 2014; Costa et al., 2015; Zhu et al., 2017).”
• Cloud-enabled prognosis for manufacturing (2015, background): “Incomplete coverage of data for training empirical models introduces additional uncertainty in data-driven approaches [159].”
• Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, methodology): “[14] proposed an ensemble data-driven prognostic approach, which employs three weighting schemes to”
• First order reliability method for time-variant problems using series expansions (2015, methodology): “…the composite limit state approach (Singh et al. 2010), the importance sampling approach (Singh et al. 2011a), and other residual life prediction methods based on testing data (Hu et al. 2012), prognostics and health management (Youn et al. 2011), and degradation knowledge (Gebraeel et al. 2009).”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “Some researchers [21,23,24,40] employed a linear data transformation method to construct a VHI by fusing multiple features.”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[120], in which the ensemble algorithms involve an SVM, a relevance vector machine (RVM), an exponential model, and a quadratic model.”