Slow-Varying Dynamics-Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation
暂无分享,去创建一个
Xiaoli Li | Chau Yuen | Yimin Shao | Yan Qin | Bo Qin
[1] N. Lu,et al. Conditional Joint Distribution-Based Test Selection for Fault Detection and Isolation , 2021, IEEE Transactions on Cybernetics.
[2] S. Ding,et al. Extended Relevance Vector Machine-Based Remaining Useful Life Prediction for DC-Link Capacitor in High-Speed Train , 2020, IEEE Transactions on Cybernetics.
[3] Zhenghua Chen,et al. Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach , 2021, IEEE Transactions on Industrial Electronics.
[4] Yan Qin,et al. Transfer Learning-Based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures , 2021, IEEE Transactions on Industrial Informatics.
[5] Yan Qin,et al. Time-Series Regeneration With Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation , 2021, IEEE Transactions on Industrial Informatics.
[6] Bin Jiang,et al. A Data-Driven Aero-Engine Degradation Prognostic Strategy , 2019, IEEE Transactions on Cybernetics.
[7] Ruqiang Yan,et al. Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder , 2021, IEEE Transactions on Instrumentation and Measurement.
[8] Abhinav Saxena,et al. Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets , 2020, International Journal of Prognostics and Health Management.
[9] Zepeng Liu,et al. Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis , 2020, IEEE Transactions on Industrial Electronics.
[10] Bing Han,et al. Data-driven based fault prognosis for industrial systems: a concise overview , 2020, IEEE/CAA Journal of Automatica Sinica.
[11] Cheng Cheng,et al. A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings , 2018, IEEE/ASME Transactions on Mechatronics.
[12] Enrique López Droguett,et al. A novel deep capsule neural network for remaining useful life estimation , 2020, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability.
[13] Enrico Zio,et al. Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture , 2019, IEEE Transactions on Industrial Electronics.
[14] Chunhui Zhao,et al. Recursive Exponential Slow Feature Analysis for Fine-Scale Adaptive Processes Monitoring With Comprehensive Operation Status Identification , 2019, IEEE Transactions on Industrial Informatics.
[15] Houxiang Zhang,et al. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture , 2019, Reliab. Eng. Syst. Saf..
[16] Chunhui Zhao,et al. Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses , 2019, IEEE Transactions on Industrial Informatics.
[17] Shuzhi Sam Ge,et al. Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery , 2018, 2019 IEEE International Conference on Industrial Technology (ICIT).
[18] W. Hager,et al. and s , 2019, Shallow Water Hydraulics.
[19] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[20] R. Sarpong,et al. Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.
[21] Xunyuan Yin,et al. Distributed State Estimation of Sensor-Network Systems Subject to Markovian Channel Switching With Application to a Chemical Process , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[22] Yaguo Lei,et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .
[23] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[24] Yi Cao,et al. Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection , 2018, IEEE Transactions on Industrial Informatics.
[25] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[26] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[27] 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.
[28] 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).
[29] Colin Bradley,et al. Hierarchical Model Predictive Image-Based Visual Servoing of Underwater Vehicles With Adaptive Neural Network Dynamic Control , 2016, IEEE Transactions on Cybernetics.
[30] Xiaoli Li,et al. Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life , 2016, DASFAA.
[31] Tianyou Zhang,et al. Health Index-Based Prognostics for Remaining Useful Life Predictions in Electrical Machines , 2016, IEEE Transactions on Industrial Electronics.
[32] Dexian Huang,et al. Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling , 2015 .
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[34] W. Marsden. I and J , 2012 .