An age- and state-dependent Markov model for degradation processes

By Massimiliano Giorgio, Maurizio Guida, Gianpaolo Pulcini. 2011

100 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Software, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/15f230236e5fed3a5df92ba5245b2271cbf51c7f

How researchers see this paper

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

In Safety, Risk, Reliability and Quality

1. Ningbo Li
2. Yaguo Lei
3. Naipeng Li
4. Zhen Hu
5. Sankaran Mahadevan
6. Xiaoping Du

In Software

1. Zhen Hu
2. Sankaran Mahadevan
3. Byeng Dong Youn

In Statistics and Probability

1. Sankaran Mahadevan
2. Zhen Hu
3. Chao Hu
4. Yaguo Lei
5. Byeng Dong Youn
6. Kyung K. Choi
7. Xiaoping Du
8. Liu Du

Citations in the corpus

• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “For example, numerous stochastic process models, including Markov models (Giorgio et al., 2011; Lee et al., 2010; Vega et al., 2021), semiMarkov models (Kharoufeh et al.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[289] established degradation models for the wear of cylinder liners by combining a Markov process and a gamma process.”