Gated Dual Attention Unit Neural Networks for Remaining Useful Life Prediction of Rolling Bearings

[1]  Wahyu Caesarendra,et al.  Detection of Deterioration of Three-phase Induction Motor using Vibration Signals , 2019 .

[2]  Li Zhao,et al.  Attention-based LSTM for Aspect-level Sentiment Classification , 2016, EMNLP.

[3]  David He,et al.  Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  X. Bai,et al.  Model-based uneven loading condition monitoring of full ceramic ball bearings in starved lubrication , 2020 .

[5]  Yi Qin,et al.  A fault dynamic model of high-speed angular contact ball bearings , 2020 .

[6]  Yi Chai,et al.  Macroscopic–Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction , 2020, IEEE Transactions on Industrial Electronics.

[7]  Lei Ren,et al.  Multi-scale Dense Gate Recurrent Unit Networks for bearing remaining useful life prediction , 2019, Future Gener. Comput. Syst..

[8]  Jehn-Ruey Jiang,et al.  Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life , 2019, Sensors.

[9]  Jong-Myon Kim,et al.  A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models , 2018, IEEE Transactions on Industrial Electronics.

[10]  Lei Ren,et al.  Bearing remaining useful life prediction based on deep autoencoder and deep neural networks , 2018, Journal of Manufacturing Systems.

[11]  Sridha Sridharan,et al.  Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection , 2017, Neural Networks.

[12]  Li Fei-Fei,et al.  Recurrent Attention Models for Depth-Based Person Identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[14]  Palaiahnakote Shivakumara,et al.  Context-Aware Attention LSTM Network for Flood Prediction , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[15]  Jiafu Wan,et al.  A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks , 2019, IEEE Transactions on Industrial Informatics.

[16]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[17]  Adam Glowacz,et al.  Acoustic fault analysis of three commutator motors , 2019, Mechanical Systems and Signal Processing.

[18]  Mohamed Tkiouat,et al.  Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network , 2018 .

[19]  Qian Liu,et al.  Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..

[20]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[21]  Hamid Reza Karimi,et al.  Vibration analysis for bearing fault detection and classification using an intelligent filter , 2014 .

[22]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[23]  Wei Zhang,et al.  Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction , 2019, Reliab. Eng. Syst. Saf..

[24]  Hong-Zhong Huang,et al.  A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions , 2019, IEEE Transactions on Industrial Electronics.

[25]  Jay Lee,et al.  Methodology for Ball Screw Component Health Assessment and Failure Analysis , 2013 .

[26]  Fucai Li,et al.  Real-Time Bearing Remaining Useful Life Estimation Based on the Frozen Convolutional and Activated Memory Neural Network , 2019, IEEE Access.

[27]  Xu Li,et al.  Data alignments in machinery remaining useful life prediction using deep adversarial neural networks , 2020, Knowl. Based Syst..

[28]  Weiwen Peng,et al.  Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network , 2019, IEEE Transactions on Industrial Electronics.

[29]  Guoqiang Zhong,et al.  Recurrent Attention Unit , 2018, ArXiv.

[30]  Alexander Hauptmann,et al.  Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[31]  Brigitte Chebel-Morello,et al.  PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .

[32]  Lovekesh Vig,et al.  Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder , 2016, ArXiv.

[33]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[34]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[35]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[36]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).