The Accuracy of the F Statistic for Structural Health Monitoring

被引:0
作者
Gunawan, Fergyanto E. [1 ]
Soewito, Benfano [2 ]
Surantha, Nico [2 ]
Mauritsius, Tuga [3 ]
Sekishita, Nobumasa [4 ]
机构
[1] Bina Nusantara Univ, Ind Engn Dept, BINUS Grad Program Master Ind Engn, Jakarta 11480, Indonesia
[2] Bina Nusantara Univ, Comp Sci Dept, BINUS Grad Program Master Comp Sci, Jakarta 11480, Indonesia
[3] Bina Nusantara Univ, Informat Syst Management Dept, BINUS Grad Program Master Informat Syst Managemen, Jakarta 11480, Indonesia
[4] Toyohashi Univ Technol, Dept Mech Engn, Toyohashi, Aichi 4418580, Japan
来源
RECENT PROGRESS ON: MECHANICAL, INFRASTRUCTURE AND INDUSTRIAL ENGINEERING | 2020年 / 2227卷
关键词
TIME-SERIES METHODS; DAMAGE; IDENTIFICATION;
D O I
10.1063/5.0000937
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.
引用
收藏
页数:7
相关论文
共 19 条
  • [1] Alamdari M. M., 2015, THESIS
  • [2] [Anonymous], 2007, SIE07001 FLIND U SCH
  • [3] Applications of neural network models for structural health monitoring based on derived modal properties
    Chang, Chia-Ming
    Lin, Tzu-Kang
    Chang, Chih-Wei
    [J]. MEASUREMENT, 2018, 129 : 457 - 470
  • [4] Farrar CR, 2012, Structural Health Monitoring: AMachine Learning Perspective
  • [5] Time-series methods for fault detection and identification in vibrating structures
    Fassois, Spilios D.
    Sakellariou, John S.
    [J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2007, 365 (1851): : 411 - 448
  • [6] Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods
    Gomes, Guilherme Ferreira
    de Almeida, Fabricio Alves
    Junqueira, Diego Morais
    da Cunha, Sebastiao Simoes, Jr.
    Ancelotti, Antonio Carlos, Jr.
    [J]. ENGINEERING STRUCTURES, 2019, 181 : 111 - 123
  • [7] Gopalakhrishnan S., 2011, COMPUTATIONAL TECHNI
  • [8] Gunawan Fergyanto Efendy, 2018, ICIC Express Letters, V12, P1183, DOI 10.24507/icicel.12.12.1183
  • [9] Gunawan FE, 2018, 2018 INDONESIAN ASSOCIATION FOR PATTERN RECOGNITION INTERNATIONAL CONFERENCE (INAPR), P62, DOI 10.1109/INAPR.2018.8627020
  • [10] Impact force reconstruction using the regularized Wiener filter method
    Gunawan, Fergyanto E.
    [J]. INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2016, 24 (07) : 1107 - 1132