Predicting remaining life by fusing the physics of failure modeling with diagnostics

By G. Kacprzynski, A. Sarlashkar, M. Roemer, A. Hess, B. Hardman. 2004

138 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/e85b7e379ecf178f382958d535ef8e4163ecf243

How researchers see this paper

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

In Civil and Structural Engineering

1. Noureddine Zerhouni
2. Kamal Medjaher

In Mechanical Engineering

1. Noureddine Zerhouni
2. Kamal Medjaher
3. Diego Alejandro Tobon-Mejia
4. Gerard Tripot
5. Naipeng Li
6. Yaguo Lei
7. Ningbo Li

In Mechanics of Materials

1. Diego Alejandro Tobon-Mejia
2. Noureddine Zerhouni
3. Gerard Tripot
4. Kamal Medjaher

Citations in the corpus

• A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models (2012, methodology): “The Paris-Erdogan law has also been used in [18] to model a crack propagation in a helicopter’s gearbox pinion, and to estimate the corresponding RUL of the component.”
• A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery (2016, methodology): “Kacprzynski et al.76 proposed the combination of Paris law along with a simplified two-dimensional (2D) finite element analysis (FEA) for RUL estimation.”