Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

A joint guidance-enhanced perceptual encoder and atrous separable pyramid-convolutions for image inpainting

(2020) Yongle Zhang et al.   NEUROCOMPUTING

Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms

(2020) Daniel Strömbergsson et al.   WIND ENERGY

Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine

(2020) Yan Xue et al.   MEASUREMENT

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

(2020) Rafia Nishat Toma et al.   SENSORS

Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network

(2020) Pengfei Liang et al.   MEASUREMENT

Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

(2020) Shijie Hao et al.   MEASUREMENT

A lightweight neural network with strong robustness for bearing fault diagnosis

(2020) Dechen Yao et al.   MEASUREMENT

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

(2020) Zhuyun Chen et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

(2020) Mien Van et al.   SENSORS

A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

(2020) Alex Shenfield et al.   SENSORS

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

(2020) Shoucong Xiong et al.   SENSORS

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

(2019) Gaowei Xu et al.   SENSORS

Atrous convolutions spatial pyramid network for crowd counting and density estimation

(2019) Junjie Ma et al.   NEUROCOMPUTING

A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings

(2019) Xianguang Kong et al.   MEASUREMENT

Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis

(2019) Jimeng Li et al.   MEASUREMENT

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

(2018) Rui Zhao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Artificial intelligence for fault diagnosis of rotating machinery: A review

(2018) Ruonan Liu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

(2018) Haidong Shao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

(2018) Wei Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

(2018) Zirui Wang et al.   NEUROCOMPUTING

Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

(2018) Dong Wang et al.   IEEE Access

Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder

(2018) Chuanhao Li et al.   IEEE Access

An enhancement denoising autoencoder for rolling bearing fault diagnosis

(2018) Zong Meng et al.   MEASUREMENT

Integrating synthetic minority oversampling and gradient boosting decision tree for bogie fault diagnosis in rail vehicles

(2018) Linlin Kou et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT

Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label

(2018) Fan Xu et al.   APPLIED SOFT COMPUTING

Deep learning and its applications to machine health monitoring

(2018) Rui Zhao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

(2017) Wei Zhang et al.   SENSORS

The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

(2017) Xiwen Qin et al.   SHOCK AND VIBRATION

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

(2017) David Verstraete et al.   SHOCK AND VIBRATION

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

(2016) Dong Wang   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach

(2015) Muhammad Amar et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

(2015) Wade A. Smith et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A summary of fault modelling and predictive health monitoring of rolling element bearings

(2015) Idriss El-Thalji et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep learning

(2015) Yann LeCun et al.   NATURE

Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

(2013) Zhipeng Feng et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

On the study of applying Morlet wavelet to the Hilbert transform for the envelope detection of bearing vibrations

(2009) Yuh-Tay Sheen   MECHANICAL SYSTEMS AND SIGNAL PROCESSING