Particle filtering prognostic estimation of the remaining useful life of nonlinear components

By Enrico Zio, Giovanni Peloni. Reliability Engineering & System Safety, 2011

352 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/16dfae09d9fa7aa399bcd71d0b47eca3348d5086

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. David He
2. Robert X. Gao
3. Soundar Kumara *
4. Jason Deutsch
5. Moneer Helu

In Control and Systems Engineering

1. Robert X. Gao
2. David He
3. David A. Dornfeld
4. Soundar Kumara *
5. M. Mori
6. Jason Deutsch
7. Roberto Teti
8. Moneer Helu

Citations in the corpus

• Cloud-enabled prognosis for manufacturing (2015, background): “Similar work can be found for the fatigue crack dynamic evolution of a mechanical component [147-148].”
• Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components (2018, methodology): “In the above DBN–FNN algorithm, step 6 is somewhat analogous to the updating step in the particle filter-based approach [35].”