Remaining useful life estimation - A review on the statistical data driven approaches

By Xiaosheng Si, Wenbin Wang, Changhua Hu, Donghua Zhou. European Journal of Operational Research, 2011

2,054 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f26c8f6754627b8d599e56480f49ab39e9b64dd7

How researchers see this paper

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

In Mechanical Engineering

1. Kwok Leung Tsui
2. Dong Wang
3. Bo Guo
4. Xiaolin Wang
5. Ruqiang Yan
6. Alireza Doostan
7. Mohammad Hadigol
8. Naipeng Li

In Safety, Risk, Reliability and Quality

1. Z. Ye
2. Qingqing Zhai
3. Kwok Leung Tsui
4. Bo Guo
5. Xiaolin Wang
6. Dong Wang
7. Alireza Doostan
8. Mohammad Hadigol

In Statistics and Probability

1. Konstantinos Nikolopoulos
2. Vassilios Assimakopoulos
3. Nikolaos Kourentzes
4. Fotios Petropoulos
5. Evangelos Spiliotis
6. Spyros Makridakis
7. Christoph Bergmeir
8. Mohammad Hadigol

Citations in the corpus

• An Overview of Artificial Intelligence Applications for Power Electronics (2020, neutral): “Another promising direction is the stochastic data-driven methods [154], [160], [168], which can intrinsically provide the pdf of the RUL for calculating”
• Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, methodology): “Therefore, data-driven methods have become the focus of RUL prediction in the world [11].”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “A fairly comprehensive survey of data-driven approaches for predicting RUL can be found in Si et al. (2011).”
• Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process (2012, methodology): “[42] presented a review on statistics-based methods for RUL estimation.”
• Tackling Climate Change with Machine Learning (2019, neutral): “[206] Xiao-Sheng Si, Wenbin Wang, Chang-Hua Hu, and Dong-Hua Zhou.”
• Least squares polynomial chaos expansion: A review of sampling strategies (2017, methodology): “Both techniques have been successfully employed to predict the RUL of batteries [110, 24, 68, 104, 103, 102, 69].”
• Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, methodology): “The RUL prediction based on the Wiener process, which is a type of statistics-based data driven methods, has gained much attention in recent years [36].”
• A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions (2015, methodology): “In the prognostic review [11, 26], one can classify the prognostic approaches into 3 groups: • model based: in this class the prognostic approaches are based on a model representing the physics of the degradation process built with specific experiments; • data based: this class includes approaches u”
• Real-time reliability evaluation of two-phase Wiener degradation process (2017, background): “Stochastic process-based modeling advocated by Pandey et al. (2009) and Singpurwalla (1995) is well known for degradation modeling in the literatures (Si et al., 2013).”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “SOH can be obtained through a complex calculation of the “black box” [16], [17].”