A multi-head neural network with unsymmetrical constraints for remaining useful life prediction

[1]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[2]  Jiansheng Guo,et al.  Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling , 2020, Journal of Systems Engineering and Electronics.

[3]  Jing-Tao Zhou,et al.  Tool remaining useful life prediction method based on LSTM under variable working conditions , 2019, The International Journal of Advanced Manufacturing Technology.

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

[5]  Shaista Hussain,et al.  Deep Recurrent Architecture with Attention for Remaining Useful Life Estimation , 2019, TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON).

[6]  Houxiang Zhang,et al.  Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture , 2019, Reliab. Eng. Syst. Saf..

[7]  Krishna R. Pattipati,et al.  Fault Prognosis of Key Components in HVAC Air-Handling Systems at Component and System Levels , 2020, IEEE Transactions on Automation Science and Engineering.

[8]  Richard Curran,et al.  Stakeholder-oriented systematic design methodology for prognostic and health management system: Stakeholder expectation definition , 2020, Adv. Eng. Informatics.

[9]  Xianke Lin,et al.  A novel transformer-based neural network model for tool wear estimation , 2020, Measurement Science and Technology.

[10]  Pierre Baldi,et al.  The dropout learning algorithm , 2014, Artif. Intell..

[11]  Zhiwu Huang,et al.  Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window , 2019, IEEE Access.

[12]  Zhiyu Zhu,et al.  A novel deep learning method based on attention mechanism for bearing remaining useful life prediction , 2020, Appl. Soft Comput..

[13]  Xianke Lin,et al.  Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach , 2021, IEEE Transactions on Industrial Informatics.

[14]  Ying Liu,et al.  Predictive maintenance using cox proportional hazard deep learning , 2020, Adv. Eng. Informatics.

[15]  Yang Xu,et al.  Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals , 2018, Complex..

[16]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[17]  Peng Wang,et al.  Long short-term memory for machine remaining life prediction , 2018, Journal of Manufacturing Systems.

[18]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[19]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[20]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[21]  Zhenghua Chen,et al.  Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach , 2021, IEEE Transactions on Industrial Electronics.

[22]  Xiaoli Li,et al.  Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life , 2016, DASFAA.

[23]  Raphael T. Haftka,et al.  Predictive airframe maintenance strategies using model-based prognostics , 2018 .

[24]  Guilin Wen,et al.  Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network , 2018, 2018 Prognostics and System Health Management Conference (PHM-Chongqing).

[25]  Yu Zheng,et al.  An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation , 2020, Comput. Ind..

[26]  Wennian Yu,et al.  Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme , 2019, Mechanical Systems and Signal Processing.

[27]  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..

[28]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[30]  Jian Ma,et al.  Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning , 2018, Complex..

[31]  Abdallah Chehade,et al.  Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis , 2019, IEEE Transactions on Automation Science and Engineering.

[32]  Lei Xiao,et al.  Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm , 2020, Neurocomputing.

[33]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[34]  Kay Chen Tan,et al.  A time window neural network based framework for Remaining Useful Life estimation , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[35]  Milton Borsato,et al.  OntoProg: An ontology-based model for implementing Prognostics Health Management in mechanical machines , 2018, Adv. Eng. Informatics.

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

[37]  Jehn-Ruey Jiang,et al.  Remaining useful life estimation using long short-term memory deep learning , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[38]  Chetan Gupta,et al.  Long Short-Term Memory Network for Remaining Useful Life estimation , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).