Statistically Filtering Data for Operational Modal Analysis under Ambient Vibration in Structural Health Monitoring Systems

Abstract

Operational modal analysis (OMA) is prevalent in large structure modal identification for that it asks for output measurements only. To guarantee identification accuracy, theoretically, OMA data need to be a random process of Gaussian white noise (GWN). Although numerous OMA applications are found in practice, few have particularly discussed the data distribution and to what extent it would blur the modal judgement. This paper presents a method to sieve segments mostly obeying the GWN distribution out of a recording. With a windowing technique, the data segments are evaluated by the modified Kurtosis value. The process has been demonstrated on the monitoring data of two case study structures: one is a laboratory truss bridge excited by artificial forces, the other is a real cable-stayed bridge subject to environmental loads. The results show that weak randomness data may result in false peaks that would possibly mislead the non-parametric modal identification, such as using the Frequency Domain Decomposition method. To overcome, cares on selecting the optimal segment shall be exercised. The proposed method is verified effective to find the most suitable data for modal identification of structural health monitoring systems.