Related references
Note: Only part of the references are listed.Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data
Yuequn Zhang et al.
SENSORS (2021)
A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory
Yangde Gao et al.
SENSORS (2021)
Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning
Tong Gao et al.
SENSORS (2020)
A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier
Levent Eren et al.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (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)
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
Te Han et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion
Xiafei Long et al.
SHOCK AND VIBRATION (2019)
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
Huihui Qiao et al.
IEEE ACCESS (2019)
A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier
Shenghan Zhou et al.
SENSORS (2018)
Medical Image Analysis using Convolutional Neural Networks: A Review
Syed Muhammad Anwar et al.
JOURNAL OF MEDICAL SYSTEMS (2018)
A multi-time scale approach to remaining useful life prediction in rolling bearing
Yuning Qian et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Wei Zhang et al.
SENSORS (2017)
Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length
Arunava Naha et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (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)
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky et al.
COMMUNICATIONS OF THE ACM (2017)
Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter
Tianzhen Wang et al.
ISA TRANSACTIONS (2016)
Thermal analysis MLP neural network based fault diagnosis on worm gears
Tayyab Waqar et al.
MEASUREMENT (2016)
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Chuan Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
Feng Jia et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM
Zhongjie Shen et al.
MEASUREMENT (2012)
Rotating machinery prognostics: State of the art, challenges and opportunities
Aiwina Heng et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)
A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM
Yu Yang et al.
MEASUREMENT (2007)
Real-time condition monitoring by significant and natural frequencies analysis of vibration signal with wavelet filter and autocorrelation enhancement
GY Luo et al.
JOURNAL OF SOUND AND VIBRATION (2000)