SGL-PCA: Health Index Construction With Sensor Sparsity and Temporal Monotonicity for Mixed High-Dimensional Signals-Reference-Cited by-同舟云学术

SGL-PCA: Health Index Construction With Sensor Sparsity and Temporal Monotonicity for Mixed High-Dimensional Signals

Published:2023-01 Issue:1 Volume:20 Page:372-384
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Wang Feng¹^ORCID,Wang Andi²^ORCID,Tang Tao³^ORCID,Shi Jianjun⁴^ORCID

Affiliation:

1. State Key Laboratory of Rail Traffic Control and Safety and the School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China

2. The Polytechnic School, Arizona State University, Mesa, AZ, USA

3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

4. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8856/10007430/09726518.pdf?arnumber=9726518

Reference27 articles.

1. Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system

2. Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

3. Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis

4. Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions

5. Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling

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