A recurrent neural network based health indicator for remaining useful life prediction of bearings

By Liang Guo, Naipeng Li, Feng Jia, Yaguo Lei, Jing Lin. Neurocomputing, 2017

1,105 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/25fd593866e1b60e03b19b6d3792e01857a5846f

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. Dhiraj Neupane
2. Jong-Hoon Seok
3. Shen Zhang
4. Bingnan Wang
5. Shibo Zhang
6. Thomas G. Habetler
7. Xiang Li
8. Enrico Zio

In Mechanical Engineering

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Olga Fink
4. Enrico Zio
5. Xiang Li
6. Naipeng Li
7. Shen Zhang
8. Bingnan Wang

In Mechanics of Materials

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Yaguo Lei
4. Naipeng Li
5. Ningbo Li
6. Liang Guo
7. Shen Zhang
8. Bingnan Wang

Citations in the corpus

• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[124].”
• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “Therefore, in order to reflect the failure progression of bearings, the feature that reflects the degree of bearing degradation is first extracted from raw degradation signals, such as root mean square, kurtosis, or crest factor [10].”
• Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, positive): “…from raw time series by their hidden layers, then followed by fully-connected layers, the representations can be mapped to outcomes of prediction tasks like machine remaining-life estimation (Zheng et al. 2017), or classification tasks like machine degradation assessment (Guo et al. 2017).”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “Several ways have been proposed to design health indicators and partly even subsequently using them for predicting RUL (Guo et al., 2017).”
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, background): “Therefore, as a variant of RNN, long-short term memory method is prefered by many researchers to prevent back-propagated errors from vanishing or exploding [46].”
• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, background): “Finding the IF point is difficult because the same machines in different conditions have a large variation in the signals [28].”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “2016) or bearing health (Guo et al. 2017), battery capacity (Shen et al.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[141] constructed a VHI for bearing prognostics by fusing multiple features using the recurrent neural network.”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “Another RNN based health indicator (RNN-HI) is proposed in [144] to predict the RUL of bearings with LSTM cells used in RNN layers.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “Moreover, [132]–[135] are some of the research carried out for MFD using a dataset other than the CWRU dataset.”