Physically based diagnosis and prognosis of cracked rotor shafts

By C. Oppenheimer, K. Loparo. SPIE Defense + Commercial Sensing, 2002

145 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/4375f5436579a787e6db7abb84608838b4729e2f

How researchers see this paper

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

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Ruqiang Yan
8. Jinjiang Wang

In Mechanics of Materials

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

Citations in the corpus

• Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks (2017, methodology): “Previous proposed models, including the Paris crack growth model [14], the Forman crack growth model [15], and so on, all belong to physics-based ones.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[192] used the Forman crack growth law [193] to predict the RUL of cracked rotor shafts.”
• Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, methodology): “The rotor shaft crack growth was modeled using Forman law of linear elastic fracture mechanics in [3].”