Direct Remaining Useful Life Estimation Based on Support Vector Regression

By Racha Khelif, Brigitte Chebel-Morello, Simon Malinowski, Emna Laajili, Farhat Fnaiech, Noureddine Zerhouni. IEEE transactions on industrial electronics (1982. Print), 2017

379 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Medical Laboratory Technology, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/7468a4847a095aed5a255f06c9398a562786b320

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. Enrico Zio
5. Liang Guo
6. Ruqiang Yan
7. Tao Yan
8. Boyuan Yang

In Medical Laboratory Technology

1. Enrico Zio

In Safety, Risk, Reliability and Quality

1. Ningbo Li
2. Yaguo Lei
3. Naipeng Li
4. Enrico Zio
5. Jianjing Zhang
6. Robert X. Gao
7. Jorge F. Arinez
8. Chengyi Xu

Citations in the corpus

• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, methodology): “Support vector regression (SVR) [12] and artifi-cial neural network (ANN) [13] are two widely used regression methods.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “Some other SVR-based prediction approaches were also developed in [39,44,53,60,343].”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “[119], an SVM has been investigated for direct RUL estimation of aircraft engine based on sensing signal features from current and past time steps and therefore, bypassing the steps of generating HI.”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, methodology): “Afterextracting someinformativefeatures,theconventionalmachinelearning algorithms,suchasartificialneuralnetwork[12],[13],ex-tremelearningmachine(ELM)[14],SVM[15],[16],neural networks[17],andrandomforest(RF)[18],canbeemployed topredicttheRUL.”