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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.
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