Remaining Useful Life Estimation of Critical Components With Application to Bearings
By Kamal Medjaher, Diego Alejandro Tobon-Mejia, Noureddine Zerhouni. IEEE Transactions on Reliability, 2012
297 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/68b2f9afbabf974986e2f71417016f289ab3ca19
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
- Noureddine Zerhouni
- Kamal Medjaher
- Dong Wang
- Kwok Leung Tsui
- Abdenour Soualhi
- Qiang Miao
- Said Rechak
- Tarak Benkedjouh
In Safety, Risk, Reliability and Quality
- Dong Wang
- Kwok Leung Tsui
- Qiang Miao
- Said Rechak
- Tarak Benkedjouh
- Noureddine Zerhouni
- Kamal Medjaher
- Z. Ye
Citations in the corpus
- Remaining useful life estimation based on nonlinear feature reduction and support vector regression (2013, background): “The analysis of the experience feedback performed on electrical machines by the Electric Power Research Institute (ERPI), and researchers in the reliability of electrical machines, has shown that the bearings and the stator are the components which present the most failures [1].”
- Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression (2015, background): “) extracted from sensors as source for getting a better understanding of bearing degradation phenomena [15]–[18].”
- Health assessment and life prediction of cutting tools based on support vector regression (2013, methodology): “The experimental platform and the data related to different cuttings are well described in the following reference (PHM Society 2010).”
- Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “The aforementioned hidden Markov model and its variants, including the semi-hidden Markov model [102], [103], coupled hidden Markov model [104], [105], adaptive hidden Markov Model [106], mixture of hidden Markov models [107] and adaptive hidden semi-Markov model [108], are also attractive for const”
- Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges (2017, methodology): “(MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool [55].”
- Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, unknown): “Prognostics and health management consists of four main modules: fault detection, fault diagnostics, fault prognostics and decision making (Medjaher et al. 2012).”