Auto-correlation-function-based damage index for damage detection and system identification

• Autokorrelationsfunktions-basierter Schadensindex zur Schadenserkennung und Systemindifikation

Zhang, Muyu; Markert, Bernd (Thesis advisor); Schmidt, Rüdiger (Thesis advisor)

Aachen (2016, 2017)
Dissertation / PhD Thesis, Report

Dissertation, RWTH Aachen University, 2016

Abstract

In this dissertation, a new approach to detect the damage based on the auto correlationfunction of vibration response signals under white noise excitation and sinusoidalexcitation is proposed. The maximum values of the auto correlation function fromdifferent measurement points are formulated as a vector called Auto Correlation Functionat Maximum Point Value Vector (AMV), which is a weighted combination of theHadamard product of two mode shapes. AMV is normalized by its root mean squarevalue to eliminate the influence of the excitation. A sensitivity analysis of the differentparts of the normalized AMV shows that the sensitivity of the normalized AMV to thelocal stiffness is dependent on the sensitivity of the Hadamard product of the two lowerorder mode shapes to the local stiffness, which has a sharp change around the localstiffness change location. The sensitivity of the normalized AMV has the same trend,which shows it is a good indicator for the damage even when the damage is very small.The relative change of the normalized AMV before and after damage is used as thedamage index to detect the damage. As a example, a stiffness reduction detection of a12-story frame structure is provided to validate the results of the sensitivity analysis,illustrate the effectiveness and anti-noise ability of the AMV-based damage detectionmethod and compare the effect of the response type and excitation frequency rangeon the detectability of the normalized AMV. Besides, comparison of the normalizedAMV and the other correlation-function-based damage detection method shows that thenormalized AMV has a better detectability. Furthermore, an auto-correlation-functionbasedsystem identification method is also presented in this dissertation.