Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

By Jaouher Ben Ali, Brigitte Chebel-Morello, L. Saidi, Simon Malinowski, Farhat Fnaiech. 2015

499 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/54afd369a90d6a285daeb2bc94a36db2bf1ccd6a

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. Biao Wang

In Mechanical Engineering

1. Yaguo Lei
2. Naipeng Li
3. Xiang Li
4. Qian Ding
5. Dong Yang
6. Weiting Zhang
7. Hongchao Wang
8. Ningbo Li

Citations in the corpus

• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, methodology): “Popular model-based approaches include particle filter [8], Eyring model [9], Weibull distribution [10] etc .”
• Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, background): “There exist several data-driven approaches such as artificial neural networks [4]–[8], autoregressive models [9], hidden Markov models [10], Bayesian networks [11], and stochastic processes [12]–[14].”
• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “[17] developed a prognostics methodology based on ANNs for accurate bearing RUL prediction.”
• Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “[121] used the Weibull distribution to fit the root mean square, kurtosis and root mean square entropy, respectively.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “Due to the complexity of the degradation processes, this dataset was treated as an optimal candidate for the research of HS division [89,90].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “In order to simplify the prognosis task, the data-driven prognostic method based on the ANN combined with the Weibull distribution was developed for achieving more accurate bearing RUL prediction [97].”