An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal

Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

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Ensemble EMD-based signal denoising using modified interval thresholding

(2017) Hongrui Wang et al.   IET Signal Processing

Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

(2017) Zhanqiang Xing et al.   Journal of Mechanical Science and Technology

A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

(2017) Haidong Shao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep neural networks-based rolling bearing fault diagnosis

(2017) Zhiqiang Chen et al.   MICROELECTRONICS RELIABILITY

A survey of deep neural network architectures and their applications

(2017) Weibo Liu et al.   NEUROCOMPUTING

An integrated generalized discriminant analysis method and chemical reaction support vector machine model (GDA-CRSVM) for bearing fault diagnosis

(2017) V.H. Nguyen et al.   Advances in Production Engineering & Management

Unsupervised feature learning and automatic modulation classification using deep learning model

(2017) Afan Ali et al.   Physical Communication

Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery

(2016) Dongyang Dou et al.   APPLIED SOFT COMPUTING

Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

(2016) Jinde Zheng   Archives of Civil and Mechanical Engineering

A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

(2016) Yongbo Li et al.   JOURNAL OF SOUND AND VIBRATION

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

(2016) Feng Jia et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Single Sample Face Recognition via Learning Deep Supervised Autoencoders

(2015) Shenghua Gao et al.   IEEE Transactions on Information Forensics and Security

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD

(2014) Yu Yang et al.   MEASUREMENT

A review of unsupervised feature learning and deep learning for time-series modeling

(2014) Martin Längkvist et al.   PATTERN RECOGNITION LETTERS

Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition

(2013) George Georgoulas et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A review on empirical mode decomposition in fault diagnosis of rotating machinery

(2012) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments

(2011) K.C. Gryllias et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE