Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

By G. S. Babu, Peilin Zhao, Xiaoli Li. International Conference on Database Systems for Advanced Applications, 2016

868 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/52b7cb1705f0815c3e66b785b3a4432b1af6d47a

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In Mechanical Engineering

Citations in the corpus

Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, methodology): “Recurrent neural networks (Hopfield, 1982; Rumelhart et al., 1988; Graves et al., 2013; Graves, 2013, especially LSTM (Gers, 1999; Graves and Schmidhuber, 2005) networks have been widely used for PHM applications (Yuan et al., 2016; Malhotra et al., 2016; Zhao et al., 2016, 2017; Wu et al., 2018).”
Remaining useful life estimation in prognostics using deep convolution neural networks (2018, methodology): “In the following, the 1D CNN is brieﬂy introduced and [31, 34, 35] can be referred to for 2D CNN.”
Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings (2017, methodology): “Babu [24] used images of two PHM competition data sets (C-MAPSS and PHM 2008) as an input to a CNN architecture.”
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks (2018, methodology): “For example, the estimation of RUL can be casted into a regression problem [33], then our proposed techniquemay be adopted to predict the target RUL value from raw input signal.”
Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “Babu et al. [32] proposed a 2D deep CNN to predict the RUL of a system based on normalised variate time series from sensor signals; they show the eﬀectiveness of the CNN in comparison to Multi-layer Perceptron (MLP), SVR and Relevance Vector Regression (RVR).”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, neutral): “Babu et al. [179] constructed 2-”
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “achieved even higher accuracy on the C-MAPSS dataset compared to both the LSTM approach in [6] and the CNN approach in [5].”
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery (2018, neutral): “To this end, authors in [12] have proposed a 2D convolutional approach by using sliding windows for RUL estimation, and [1] has proposed LSTM networks for RUL estimation.”
Long Short-Term Memory Network for Remaining Useful Life estimation (2017, methodology): “We tried several different network structures for MLP, CNN and LSTM, and found these were the best structures.”
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, methodology): “[29] treated RUL prediction as multivariate time series regression”
A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, methodology): “, DCNN [16], [40] and MODBNE [20]) are network architecture and model inputs.”
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, methodology): “For the algorithms of SVR, RF, and CNN, we use the parameters in [23] and [26], which have carefully tuned the parameters of the models.”