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。A combination of residual and long-short-term memory networks for bearing fault diagnosis based on time-series model analysis
Youming Wang et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2021)
Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals
Zhenya Wang et al.
ISA TRANSACTIONS (2021)
Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis
Minghang Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)
A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network
Jing Zhao et al.
MEASUREMENT (2021)
Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network With Residual Connection
Haopeng Liang et al.
IEEE ACCESS (2021)
Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network
Qingwen Guo et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Multibranch and Multiscale CNN for Fault Diagnosis of Wheelset Bearings Under Strong Noise and Variable Load Condition
Dandan Peng et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Deep Residual Shrinkage Networks for Fault Diagnosis
Minghang Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions
Ruonan Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Squeeze-and-Excitation Networks
Jie Hu et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)
Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis
Zhenya Wang et al.
RENEWABLE ENERGY (2020)
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Guoqian Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis
Weiguo Huang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)
On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring
Jian Chen et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method
Zenghui An et al.
NEUROCOMPUTING (2019)
Deep residual learning-based fault diagnosis method for rotating machinery
Wei Zhang et al.
ISA TRANSACTIONS (2019)
Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis
Wenjun Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (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)
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)
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai et al.
TRIBOLOGY INTERNATIONAL (2016)
Rolling bearing fault diagnosis using an optimization deep belief network
Haidong Shao et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2015)