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.
Highlights
Operational modal analysis (OMA) has been extensively applied in modal tests of practical structures due to its convenient output-only merit
Relevant applications include an ancient Cypriot aqueduct built in 1747 [4], a Tunisian temple built in16th century [5], [6], and an Egyptian Al-Sultaniyamirarets built in 1337 [7].the rapid increase of the research in theory and applications of this area has motivated the creation of the International Operational Modal Analysis Conference (IOMAC) since 2005
No matter whether a method is in time or frequency domain, one assumption of the OMA technique must be fulfilled: the responses of the underlying structure excited by natural loads are Gaussian White Noise (GWN)
Summary
Operational modal analysis (OMA) has been extensively applied in modal tests of practical structures due to its convenient output-only merit. No matter whether a method is in time or frequency domain, one assumption of the OMA technique must be fulfilled: the responses of the underlying structure excited by natural loads are Gaussian White Noise (GWN). In order to check the distribution characteristics of the output measurement and help analysts select the data segments that most likely have reliable identification results. To meet this need, the paper proposes a statistical index of the modified kurtosis to judge data randomness. Optimal data segments are filtered out in both case studies
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