Basic Consideration for Accuracy Improvement of TPA Method Employing Running Data

Abstract

In this study, we considered a calculation method to obtain accurately transfer functions employed in running transfer path analysis using principal component regression method. In the running transfer path analysis method, extracting noise components correctly in principal components, which consist of transfer functions, is important. Then, we tried to apply statistical verification method for extracting the noise components in the principal components. In the method, we set probability significance level of each principal component toward output signal as noise reduction standard instead of using the size of principal component that is used in conventional method. Subsequently, we verified the noise reduction performance through simple simulation and compared the performance of the statistical method and that of the conventional method. As results, noise influence was reduced from calculated transfer function by applying the statistical method effectively better than that of the conventional method. From these analytical results, it was clarified that the noise reduction method using statistical verification method has ability to obtain accurately transfer functions and considered that using the new method will increase the accuracy of running transfer path analysis method.

Structural health monitoring for layered structures using transmissibility functions

“…The unmeasured transmissibility function or frequency response function is then estimated. Transfer path analysis (Yoshida and Noumura, 2011) is also based on this idea. In methods based on the Local TF, an external force acts on single DOF, and various transmissibility functions are measured.…”