Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

Shuyang Luo; Xufeng Huang; Yanzhi Wang; Rongmin Luo; Qi Zhou

doi:10.1016/j.knosys.2022.109846

ScienceGate Book Chapters

JOURNAL ARTICLE

Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

Shuyang Luo Xufeng Huang Yanzhi Wang Rongmin Luo Qi Zhou

Year: 2022 Journal: Knowledge-Based Systems Vol: 256 Pages: 109846-109846 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2022.109846

Get Full-Text PDF Get Analytical Report

Keywords:

Autoencoder Transfer of learning Artificial intelligence Computer science Deep learning Context (archaeology) Domain (mathematical analysis) Feature (linguistics) Fault (geology) Divergence (linguistics) Pattern recognition (psychology) Machine learning Mathematics

Metrics

Cited By

14.48

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Machine Fault Diagnosis Techniques

Physical Sciences → Engineering → Control and Systems Engineering

Gear and Bearing Dynamics Analysis

Physical Sciences → Engineering → Mechanical Engineering

Occupational Health and Safety Research

Health Sciences → Health Professions → Radiological and Ultrasound Technology

Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

Metrics

Citation History

Topics

Related Documents

Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate

Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Stacked Ensemble Learning-Based Bearing Fault Diagnosis

Bearing fault diagnosis method based on stacked autoencoder and softmax regression

Quantum stacked autoencoder fault diagnosis model for bearing faults