A New Deep Tensor Autoencoder Network for Unsupervised Health Indicator Construction and Degradation State Evaluation of Metro Wheel-Reference-Cited by-同舟云学术

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A New Deep Tensor Autoencoder Network for Unsupervised Health Indicator Construction and Degradation State Evaluation of Metro Wheel

Published:2023 Issue: Volume:72 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Mao Wentao¹^ORCID,Wang Yu¹^ORCID,Kou Linlin²^ORCID,Liang Xihui³^ORCID

Affiliation:

1. School of Computer and Information Engineering, Henan Normal University, Xinxiang, China

2. Technology Department, Beijing Mass Transit Railway Operation Corporation Ltd., Beijing, China

3. Department of Mechanical Engineering, University of Manitoba, Winnipeg, Canada

Funder

National Natural Science Foundation of China

Henan Province Technologies Research and Development Project of China

University Research Grants Program

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10057446.pdf?arnumber=10057446

Reference48 articles.

1. Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

2. Three-way unsupervised data mining for power system applications based on tensor decomposition

3. Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks

4. Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

5. Canonical correlation analysis of dimension reduced degradation feature space for machinery condition monitoring

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5. Operation Condition Assessment for Elevators Based on Deep Siamese Network and T-S Semi-Supervision Model;IEEE Transactions on Instrumentation and Measurement;2024

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