Remaining useful life estimation based on nonlinear feature reduction and support vector regression

By Tarak Benkedjouh, Kamal Medjaher, Noureddine Zerhouni, Said Rechak. Engineering applications of artificial intelligence, 2013

324 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/f67ccd19962f72f9686a134242227580a3976e41

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. Xiang Li
5. Qian Ding
6. Liang Guo
7. Tao Yan
8. Jing Lin

In Mechanical Engineering

1. Naipeng Li
2. Yaguo Lei
3. Paulo da Costa
4. Uzay Kaymak
5. Yingqian Zhang
6. Alp Eren Akçay
7. Xiang Li
8. Qian Ding

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Xiang Li
8. Jianqiao Sun

Citations in the corpus

• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[75,128] used the SVR to map the HIs into nonlinear regressions, and then fitted the obtained regressions into power models for RUL prediction of machinery.”
• Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network (2015, unknown): “In [16], the Isometric feature Mapping reduction technique (ISOMAP) and Support Vector Regression (SVR) are used for prognostics of bearing fault degradations.”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, unknown): “[116], SVM was investigated for bearing prognosis.”
• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, background): “, neurofuzzy systems [30], [31], or statistical learning and pattern recognition tools [32], [33], e.”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, unknown): “Withdomainknowledge onmechanicalsystems,somerepresentativefeaturescanbe extractedfromrawsensorydatathatmaybenoisyandnot representativefortheRULprediction[10],[11].”
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, background): “…on much prior expertise on prognostics and are easy to be generalized, many data-driven algorithms have been proposed in the recent years and good prognostic results have been achieved, including artificial neural network (NN) [11], support vector machine (SVM) [12], hidden Markov models [13] etc .”
• Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression (2015, methodology): “This is obtained by using well-established techniques for linear/nonlinear dimensionality (feature) reduction such as the principal component analysis and ISOMAP [26].”
• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “In particular, authors have proposed several methods that attempt to extract the relationships of acquired sequential data and RUL prediction, such as linear regression [23], Support Vector Regression (SVR) [8], fuzzy-logic systems [24] and neural networks [25].”