Machinery health prognostics: A systematic review from data acquisition to RUL prediction

By Yaguo Lei, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, Jing Lin. 2018

2,251 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/dae85fed5fab9bb37f2a1713dd7f8933f4f6fe06

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In Control and Systems Engineering

In Mechanics of Materials

In Safety, Risk, Reliability and Quality

Citations in the corpus

XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial (2019, neutral): “[1] LEI Yaguo,LI Naipeng,GUO Liang,et al.”
Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “A constant R e of 125 cycles is selected in our experiments to allow comparison to other proposed models in the literature [11, 5].”
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, background): “and expensive to obtain fault data of machines [12].”
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, background): “More data-driven approaches can be found in a recent review paper [20].”
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, background): “Recently, data-driven solutions have attracted more and more attention for RUL prediction [4].”
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021, background): “As multiple research studies have pointed out [3–7], the CM data provide an untapped potential to develop data-driven algorithms for various predictive maintenance applications.”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “Although the process of data collection is clear and intuitive, there are still difficulties for acquiring high-quality data in real industrial scenarios [20].”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “260 For more information about RUL estimation, interested readers are recommended to read the bottom-top review paper by Lei et al., 261 and the deep NN methods for RUL estimation by Zhang et al. 262 However, the biggest challenge in achieving complete 5-stage damage identi ﬁ cation is creating a…”