相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere
Dusan Strusnik
INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2022)
A new tool wear condition monitoring method based on deep learning under small samples
Yuqing Zhou et al.
MEASUREMENT (2022)
A novel entropy-based sparsity measure for prognosis of bearing defects and development of a sparsogram to select sensitive filtering band of an axial piston pump
Yuqing Zhou et al.
MEASUREMENT (2022)
Fault Diagnosis and Prognosis of Aerospace Systems Using Growing Recurrent Neural Networks and LSTM
Musab ElDali et al.
2021 IEEE AEROSPACE CONFERENCE (AEROCONF 2021) (2021)
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network
Jun Wu et al.
ISA TRANSACTIONS (2020)
Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model
Mingming Yan et al.
ISA TRANSACTIONS (2020)
A survey of regularization strategies for deep models
Reza Moradi et al.
ARTIFICIAL INTELLIGENCE REVIEW (2020)
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Biao Wang et al.
IEEE TRANSACTIONS ON RELIABILITY (2020)
Remaining useful life prediction using multi-scale deep convolutional neural network
Han Li et al.
APPLIED SOFT COMPUTING (2020)
Towards Distribution Clustering-Based Deep LSTM Models for RUL Prediction
Mohamed Sayah et al.
2020 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-BESANCON 2020) (2020)
State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method
Bin Gou et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)
Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries
YongZhi Zhang et al.
JOURNAL OF CLEANER PRODUCTION (2019)
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Andre Listou Ellefsen et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2019)
Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
Xiaoyu Li et al.
JOURNAL OF ENERGY STORAGE (2019)
A new ensemble residual convolutional neural network for remaining useful life estimation
Long Wen et al.
MATHEMATICAL BIOSCIENCES AND ENGINEERING (2019)
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)
An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth
Diana Barraza-Barraza et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Chong Zhang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)
A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery
Adrian Cubillo et al.
ADVANCES IN MECHANICAL ENGINEERING (2016)
Stochastic and nonlinear-based prognostic model
Khaled El-Tawil et al.
SYSTEMS SCIENCE & CONTROL ENGINEERING (2013)
Life Prediction for Turbopropulsion Systems Under Dwell Fatigue Conditions
Kwai S. Chan et al.
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME (2012)
A generic probabilistic framework for structural health prognostics and uncertainty management
Pingfeng Wang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)
Remaining useful life estimation - A review on the statistical data driven approaches
Xiao-Sheng Si et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2011)