Dr. Atsushi Iwasaki Profile

Dr. Atsushi Iwasaki

Research Associate at Gunma Univ

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Publications (2)

Proceedings Article | 10 April 2007 Paper

Statistical damage diagnosis of in-service structure under high noise environment using multiple reference data

Atsushi Iwasaki, Akira Todoroki, Yoshinobu Shimamura

Proceedings Volume 6530, 65301I (2007) https://doi.org/10.1117/12.715572

KEYWORDS: Diagnostics, Fluctuations and noise, Sensors, Data modeling, System identification, Inspection, Damage detection, Structural health monitoring, Mechanical engineering, Finite element methods

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For the health monitoring of existing structures, modeling of entire structure or obtaining data sets after creating damage for training is almost impossible. This raises significant demand for development of a low-cost diagnostic method that does not require modeling of entire structure or data on damaged structure. Therefore, the present study proposes a low-cost statistical diagnostic method for structural damage detection. The novel statistical diagnostic method is a low cost simple system. The diagnostic method employs system identification using a response surface and the damage is automatically diagnosed by testing the change of the identified system by statistical F test. The statistical diagnostic method consists of a learning mode and a monitoring mode. The learning mode is a preparation mode and is performed to create the standard of the diagnosis. The monitoring mode is a diagnosis mode and is performed to diagnose the structural condition. In the learning mode, reference data are measured from an intact structure. A reference response surface is calculated from the reference data using the response surface method. In the monitoring mode, data are measured from a structure to diagnose and a measured response surface is calculated. The statistical similarity of the reference response surface and the measured response surface is tested using the F-test for the damage diagnosis. When the similarity of the response surfaces is adopted, a conclusion of the diagnosis is intact condition. On the other hand, when the similarity is rejected, the diagnosis concludes the structure was damaged. The system does not require the relation between measured sensor data and damages. The method does not require a FEM model of the entire structure. This method diagnoses slight change of the relation between the measured sensor data. In this study, the health monitoring system of the jet fan was developed to investigate the effectiveness of the proposed method. In this study, field test was conducted using an actual jet fan in a tunnel. In the field test, robustness of the proposed method was investigated. As a result, the structural condition of the jet fan was successfully diagnosed and effectiveness of proposed method was confirmed.

Proceedings Article | 21 July 2004 Paper

Damage diagnosis for SHM of existing civil structure with statistical diagnostic method

Atsushi Iwasaki, Akira Todoroki, Tsuneya Sugiya, Shinsuke Sakai

Proceedings Volume 5394, (2004) https://doi.org/10.1117/12.539467

KEYWORDS: Diagnostics, Sensors, Structural health monitoring, System identification, Data modeling, Damage detection, Fluctuations and noise, Artificial neural networks, Mechanical engineering, Smart structures

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