Related references
Note: Only part of the references are listed.A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals
Miao He et al.
NEUROCOMPUTING (2020)
Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network
Zhong-Xu Hu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions
Xiaoan Yan et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis
Jianbo Yu et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Guoqian Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Siyu Shao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals
Huaqing Wang et al.
COMPUTERS IN INDUSTRY (2019)
Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines
Jinrui Wang et al.
NEUROCOMPUTING (2019)
Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform
Renxiang Chen et al.
COMPUTERS IN INDUSTRY (2019)
Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network
Chunzhi Wu et al.
COMPUTERS IN INDUSTRY (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)
Residual convolutional neural network with attentive feature pooling for end-to-end language identification from short-duration speech
Joao Monteiro et al.
COMPUTER SPEECH AND LANGUAGE (2019)
Deep residual learning-based fault diagnosis method for rotating machinery
Wei Zhang et al.
ISA TRANSACTIONS (2019)
Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis
Jinyang Jiao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Deep learning and its applications to machine health monitoring
Rui Zhao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
A survey on Deep Learning based bearing fault diagnosis
Duy-Tang Hoang et al.
NEUROCOMPUTING (2019)
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Long Wen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing
Xiaoan Yan et al.
NEUROCOMPUTING (2018)
Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine
Ruonan Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Osama Abdeljaber et al.
JOURNAL OF SOUND AND VIBRATION (2017)
Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network
Zhuyun Chen et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
Turker Ince et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach
Muhammad Amar et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
Bearing performance degradation assessment using locality preserving projections
Jian-Bo Yu
EXPERT SYSTEMS WITH APPLICATIONS (2011)
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Long Zhang et al.
EXPERT SYSTEMS WITH APPLICATIONS (2010)
Fault classification of rolling bearing based on reconstructed phase space and Gaussian mixture model
Guo Feng Wang et al.
JOURNAL OF SOUND AND VIBRATION (2009)