Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

By Ahmed Mosallam, Kamal Medjaher, Noureddine Zerhouni. Journal of Intelligent Manufacturing, 2014

325 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/b1c05c773db611f45e390facfc169b7c1277f245

How researchers see this paper

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

In Automotive Engineering

1. Yaguo Lei
2. Ningbo Li
3. Naipeng Li

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Noureddine Zerhouni
8. Shen Zhang

In Safety, Risk, Reliability and Quality

1. Ningbo Li
2. Yaguo Lei
3. Naipeng Li
4. Noureddine Zerhouni

Citations in the corpus

• Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, background): “There exist several data-driven approaches such as artificial neural networks [4]–[8], autoregressive models [9], hidden Markov models [10], Bayesian networks [11], and stochastic processes [12]–[14].”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “[26,43] proposed a consistency metric based on the pairwise symmetrical uncertainty.”