Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

By Linxia Liao, F. Kottig. IEEE Transactions on Reliability, 2014

495 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/639aa42e83d9b716c1751d2ddba840b77a3dd105

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. Jooho Choi
2. Dawn An
3. Yaguo Lei
4. Ningbo Li
5. Naipeng Li
6. Nam-Ho Kim
7. Biao Wang

In Control and Systems Engineering

1. Ningbo Li
2. Naipeng Li
3. Biao Wang
4. Yaguo Lei
5. Olga Fink
6. Kai F. Goebel
7. Manuel Arias Chao
8. Jooho Choi

In Safety, Risk, Reliability and Quality

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao
4. Ningbo Li
5. Naipeng Li
6. Yaguo Lei
7. Jooho Choi
8. Dawn An

Citations in the corpus

• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “In addition, the performance of a data-driven approach also largely depends on the parameter selection of the adopted ML model [19].”
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, methodology): “[18] and Liu et al.”
• Fusing Physics-based and Deep Learning Models for Prognostics (2020, background): “Some examples of hybrid models for prognostics are [18, 19, 20, 21, 22, 23].”