The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

Rolling bearing fault diagnosis is a meaningful and challenging task. Most methods first extract statistical features and then carry out fault diagnosis. At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer...

Full description

Saved in:

Bibliographic Details
Main Authors:	Xiwen Qin, Dingxin Xu, Xiaogang Dong, Xueteng Cui, Siqi Zhang
Format:	Article
Language:	English
Published:	Wiley 2021-01-01
Series:	Shock and Vibration
Online Access:	http://dx.doi.org/10.1155/2021/9933137
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
by: Hongmei Liu, et al.
Published: (2016-01-01)

An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
by: Zhicheng Qiao, et al.
Published: (2020-01-01)

Rolling Bearing Fault Diagnosis Based on SVM Optimized with Adaptive Quantum DE Algorithm
by: Yuanyuan Li, et al.
Published: (2022-01-01)

Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals
by: Yong Chang, et al.
Published: (2021-06-01)

Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM
by: Yanwei Xu, et al.
Published: (2021-01-01)

Rolling Bearing Fault Diagnosis Method Based on MCMF and SAIMFE
by: Dejun Meng, et al.
Published: (2022-01-01)

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis
by: Jinde Zheng, et al.
Published: (2014-01-01)

A study on rolling bearing fault diagnosis using RIME-VMD
by: Zhenrong Ma, et al.
Published: (2025-02-01)

Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
by: Ruqiang Yan, et al.
Published: (2015-01-01)

Rolling bearing fault diagnosis based on parameter optimized VMD and improved GoogLeNet
by: LI Haoran, et al.
Published: (2025-01-01)

Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM
by: Jiang Xingmeng, et al.
Published: (2016-01-01)

An Improved Dual-Kurtogram-Based T2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings
by: Zhiyuan Jiao, et al.
Published: (2021-01-01)

A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing
by: Jinyu Tong, et al.
Published: (2020-01-01)

Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model
by: Wei Yuan, et al.
Published: (2024-01-01)

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features
by: Weigang Wen, et al.
Published: (2015-01-01)

Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition
by: Bo Zhou, et al.
Published: (2016-01-01)

Rolling Bearings Fault Diagnosis under Variable Speed Conditions Based on Multitime-Frequency Ridge Extraction
by: Qing Xiong, et al.
Published: (2024-01-01)

Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier
by: Te Han, et al.
Published: (2016-01-01)

A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis
by: Yonggang Xu, et al.
Published: (2019-01-01)

Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning
by: Guodong Sun, et al.
Published: (2021-01-01)

Intelligent fault diagnosis method for rolling bearings based on flexible residual neural network
by: Chuang CHEN, et al.
Published: (2025-03-01)

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis
by: Hongdi Zhou, et al.
Published: (2022-01-01)

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation
by: Xing Yuan, et al.
Published: (2022-01-01)

Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network
by: Yanli Yang, et al.
Published: (2018-01-01)

Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition
by: Bo Qin, et al.
Published: (2021-01-01)

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter
by: Yuanqing Luo, et al.
Published: (2019-01-01)

Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions
by: Xiao Yu, et al.
Published: (2021-01-01)

Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification
by: Mingxing Jia, et al.
Published: (2020-01-01)

Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
by: Shiyuan Liu, et al.
Published: (2020-01-01)

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine
by: Mingliang Liang, et al.
Published: (2018-01-01)

Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA
by: WU Xinzhong, et al.
Published: (2024-12-01)

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions
by: Yujing Huang, et al.
Published: (2022-01-01)

Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise
by: Hongwei Fan, et al.
Published: (2021-01-01)

A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis
by: Xinghui Zhang, et al.
Published: (2015-01-01)

State-of-the-Art Detection and Diagnosis Methods for Rolling Bearing Defects: A Comprehensive Review
by: Bojun Sun, et al.
Published: (2025-01-01)

Multidomain Feature Fusion Network for Fault Diagnosis of Rolling Machinery
by: Dewei Yang, et al.
Published: (2022-01-01)

Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network
by: Zihao Zhai, et al.
Published: (2025-01-01)

A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
by: Omar AlShorman, et al.
Published: (2020-01-01)

Deep Domain Adaptation Approach Using an Improved Parallel Residual Network for Cross-Domain Bearing Fault Diagnosis
by: Jiezhou Huang
Published: (2024-01-01)

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing
by: Xiaolong Wang, et al.
Published: (2020-01-01)