Foundations of Population-based SHM, Part II: Heterogeneous populations – Graphs, networks, and communities-Reference-Cited by-同舟云学术

Foundations of Population-based SHM, Part II: Heterogeneous populations – Graphs, networks, and communities

Published:2021-02 Issue: Volume:148 Page:107144
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Gosliga J.,Gardner P.A.,Bull L.A.,Dervilis N.,Worden K.

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference28 articles.

1. Foundations of population-based SHM, Part I: homogeneous populations and forms. Submitted to;Bull;Mechanical Systems and Signal Processing,2020

2. Structural Health Monitoring: A Machine Learning Perspective;Farrar,2012

3. A. Rytter, Vibrational based inspection of civil engineering structures. PhD thesis, Deptartment of Building Technology and Structural Engineering, Aalborg University, Denmark, 1993.

4. Aspects of structural health and condition monitoring of offshore wind turbines;Antoniadou;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,2015

5. A performance monitoring approach for the novel Lillgrund offshore wind farm;Papatheou;IEEE Transactions on Industrial Electronics,2015

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