Cargando…

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

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis met...

Descripción completa

Detalles Bibliográficos
Autores principales:	Shi, Hongmei, Chen, Jingcheng, Si, Jin, Zheng, Changchang
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600409/ https://www.ncbi.nlm.nih.gov/pubmed/33050210 http://dx.doi.org/10.3390/s20205734

Ejemplares similares

Sparse Convolutional Denoising Autoencoders for Genotype Imputation
por: Chen, Junjie, et al.
Publicado: (2019)

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder
por: Xu, Xiaowei, et al.
Publicado: (2021)

A Novel Fault Diagnosis Method for Denoising Autoencoder Assisted by Digital Twin
por: Cai, Wenan, et al.
Publicado: (2022)

Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network
por: Zhang, Xiong, et al.
Publicado: (2023)

An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis
por: Xu, Meng, et al.
Publicado: (2023)

Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder
por: Jung, Yuyeon, et al.
Publicado: (2022)

Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network
por: Yan, Jialin, et al.
Publicado: (2022)

Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images
por: Solovyeva, Elena, et al.
Publicado: (2022)

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
por: Toma, Rafia Nishat, et al.
Publicado: (2021)

Author Correction: Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder
por: Jung, Yuyeon, et al.
Publicado: (2023)

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
por: Duan, Ruifeng, et al.
Publicado: (2023)

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions
por: Liu, Hengchang, et al.
Publicado: (2019)

Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings
por: Xie, Shenglong, et al.
Publicado: (2020)

MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network
por: Zhang, Feiqing, et al.
Publicado: (2023)

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform
por: Pang, Bin, et al.
Publicado: (2018)

Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM
por: Zhou, Junbo, et al.
Publicado: (2022)

A Convolutional Autoencoder Based Fault Diagnosis Method for a Hydraulic Solenoid Valve Considering Unknown Faults
por: Yoo, Seungjin, et al.
Publicado: (2023)

A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples
por: Peng, Cheng, et al.
Publicado: (2022)

Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis
por: Kanno, Yasuhiro, et al.
Publicado: (2022)

Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data
por: Gareev, Albert, et al.
Publicado: (2021)

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder
por: Yan, Xiaoan, et al.
Publicado: (2021)

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
por: Mei, Shuang, et al.
Publicado: (2018)

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings
por: Duan, Jie, et al.
Publicado: (2018)

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings
por: Zhou, Hongdi, et al.
Publicado: (2017)

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM
por: Ye, Maoyou, et al.
Publicado: (2021)

Fault Diagnosis of Rolling Bearings Based on WPE by Wavelet Decomposition and ELM
por: Xi, Caiping, et al.
Publicado: (2022)

Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method
por: Xiang, Chuan, et al.
Publicado: (2023)

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis
por: Song, Xinmin, et al.
Publicado: (2023)

On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures
por: Tellaeche Iglesias, Alberto, et al.
Publicado: (2021)

Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
por: Wang, Changmiao, et al.
Publicado: (2018)

Application of Convolutional Neural Network in Motor Bearing Fault Diagnosis
por: Zhou, Shuiqin, et al.
Publicado: (2022)

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures
por: Xiong, Shoucong, et al.
Publicado: (2020)

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum
por: Lv, Yong, et al.
Publicado: (2015)

Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing
por: Dang, Zhang, et al.
Publicado: (2018)

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition
por: Cheng, Yujie, et al.
Publicado: (2017)

The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings
por: Sun, Peng, et al.
Publicado: (2017)

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis
por: Zheng, Kai, et al.
Publicado: (2020)

A Novel Fault Diagnosis Method of Rolling Bearings Based on AFEWT-KDEMI
por: Ge, Mingtao, et al.
Publicado: (2018)

Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing
por: Xu, Yonggang, et al.
Publicado: (2019)

Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning
por: Wang, Sihan, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_jrko6kr604n8m52tc0vnml7mq5