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。Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis
Shaowei Liu et al.
MEASUREMENT (2021)
An intelligent diagnosis framework for roller bearing fault under speed fluctuation condition
Baokun Han et al.
NEUROCOMPUTING (2021)
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
Yalin Wang et al.
ISA TRANSACTIONS (2020)
A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine
Kun Chen et al.
SENSORS (2020)
Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions
Xiaoan Yan et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network
You-ren Wang et al.
APPLIED SOFT COMPUTING (2020)
An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm
Xingqiu Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Macroscopic-Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction
Yi Qin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm
Fan Xu et al.
SOFT COMPUTING (2019)
Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
Gabriel San Martin et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)
Fault diagnosis of rolling element bearing based on artificial neural network
Rohit S. Gunerkar et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2019)
Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network
Xingqiu Li et al.
MECHANISM AND MACHINE THEORY (2019)
Generative adversarial networks for data augmentation in machine fault diagnosis
Siyu Shao et al.
COMPUTERS IN INDUSTRY (2019)
Evolutionary manifold regularized stacked denoising autoencoders for gearbox fault diagnosis
Jian-Bo Yu
KNOWLEDGE-BASED SYSTEMS (2019)
Multi-label classification using a cascade of stacked autoencoder and extreme learning machines
Anwesha Law et al.
NEUROCOMPUTING (2019)
Enhanced generative adversarial networks for fault diagnosis of rotating machinery with imbalanced data
Qi Li et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2019)
Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions
Sai Ma et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder
Wei Jiang et al.
ISA TRANSACTIONS (2019)
Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain
Xian-Bo Wang et al.
MEMETIC COMPUTING (2019)
Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data
Meng Ma et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Fault diagnosis of rolling bearings with recurrent neural network based autoencoders
Han Liu et al.
ISA TRANSACTIONS (2018)
A comparison of fuzzy clustering algorithms for bearing fault diagnosis
Chuan Li et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2018)
Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
Shao Haidong et al.
KNOWLEDGE-BASED SYSTEMS (2018)
Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals
Ge Xin et al.
MEASUREMENT (2018)
Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network
Hongkai Jiang et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2018)
Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier
Jun Yu et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2018)
Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines
M. M. Manjurul Islam et al.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2017)
A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection
Zexian Wei et al.
KNOWLEDGE-BASED SYSTEMS (2017)
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
Luyang Jing et al.
MEASUREMENT (2017)
Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
Yumei Qi et al.
IEEE ACCESS (2017)
Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher's criterion
Ridha Ziani et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2017)
A new bio-inspired optimisation algorithm: Bird Swarm Algorithm
Xian-Bing Meng et al.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE (2016)
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
Xiaojie Guo et al.
MEASUREMENT (2016)
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
Wenjun Sun et al.
MEASUREMENT (2016)
An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks
Van Tung Tran et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
分享论文
