A deep transferable motion-adaptive fault detection method for industrial robots using a residual–convolutional neural network

Yeongtak Oh; Yunhan Kim; Kyumin Na; Byeng D. Youn

doi:10.1016/j.isatra.2021.11.019

ScienceGate Book Chapters

JOURNAL ARTICLE

A deep transferable motion-adaptive fault detection method for industrial robots using a residual–convolutional neural network

Yeongtak Oh Yunhan Kim Kyumin Na Byeng D. Youn

Year: 2021 Journal: ISA Transactions Vol: 128 (Pt B)Pages: 521-534 Publisher: Elsevier BV

DOI: 10.1016/j.isatra.2021.11.019

Get Full-Text PDF Get Analytical Report

Keywords:

Artificial intelligence Convolutional neural network Robot Fault detection and isolation Industrial robot Computer science Fault (geology) Artificial neural network Engineering Control engineering Computer vision Actuator

Metrics

Cited By

3.19

FWCI (Field Weighted Citation Impact)

Refs

0.93

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

Structural Health Monitoring Techniques

Physical Sciences → Engineering → Civil and Structural Engineering

A deep transferable motion-adaptive fault detection method for industrial robots using a residual–convolutional neural network

Metrics

Citation History

Topics

Related Documents

Motion-Adaptive Few-Shot Fault Detection Method of Industrial Robot Gearboxes via Residual Convolutional Neural Network

A grasp detection method for industrial robots using a Convolutional Neural Network

Transferable deep convolutional neural network features for fingervein presentation attack detection

Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network

Fault prognosis using deep convolutional neural network and bootstrap-based method