A Robust Semi-Supervised Fisher Discriminant Analysis for Industrial Fault Classification-Reference-Cited by-同舟云学术

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A Robust Semi-Supervised Fisher Discriminant Analysis for Industrial Fault Classification

Published:2024-02-15 Issue:4 Volume:24 Page:4735-4745
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Liu Jun¹,Yao Jiayang²,Jiang Peng¹^ORCID,Song Chunyue³^ORCID

Affiliation:

1. School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China

2. Chaochuang Digital Energy Technology Company Ltd., Hangzhou, China

3. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, China

Funder

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

“Ling-Yan” Research and Development Project of Zhejiang Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10433867/10380509.pdf?arnumber=10380509

Reference39 articles.

1. Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

2. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data

3. Modified Varying Index Coefficient Autoregression Model for Representation of the Nonstationary Vibration From a Planetary Gearbox

4. A time series model-based method for gear tooth crack detection and severity assessment under random speed variation

5. Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data

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