Intelligent Fault Quantitative Identification for Industrial Internet of Things (IIoT) via a Novel Deep Dual Reinforcement Learning Model Accompanied With Insufficient Samples-Reference-Cited by-同舟云学术

Intelligent Fault Quantitative Identification for Industrial Internet of Things (IIoT) via a Novel Deep Dual Reinforcement Learning Model Accompanied With Insufficient Samples

Published:2022-10-15 Issue:20 Volume:9 Page:19811-19822
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Chang Yuanhong¹^ORCID,Chen Jinglong¹^ORCID,Wu Wenyang¹,Pan Tongyang¹^ORCID,Zhou Zitong²^ORCID,He Shuilong³^ORCID

Affiliation:

1. State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an, China

2. FAST Auto Drive Transmission Engineering Research Institute, Shaanxi Fast Gear Company Ltd, Xi’an, China

3. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Scientific Research and Technology Development in Liuzhou

Open Fund of State Key Laboratory

Guangxi Natural Science Foundation Program

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9913253/09759483.pdf?arnumber=9759483

Reference30 articles.

1. Parametric nonlinear dimensionality reduction using kernel t-SNE

2. Hybrid Cross Deep Network for Domain Adaptation and Energy Saving in Visual Internet of Things

3. A Compact Convolutional Neural Network Augmented with Multiscale Feature Extraction of Acquired Monitoring Data for Mechanical Intelligent Fault Diagnosis

4. Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels

5. Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

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