ENHANCED MODE SHAPE ESTIMATION IN MULTIDATASET OMA USING FREQUENCY DOMAIN DECOMPOSITION

Diord Rescinho Amador, S. (Guest lecturer)

Activity: Talks and presentationsConference presentations

Description

The identification of the global mode shapes in multi-dataset vibration tests with the classic Frequency Domain Decomposition is carried out by identifying each dataset individually and by merging the individual mode shape parts with aid of the reference mode shape components. It turns out that, in case of
closely spaced modes, the mode shape parts in the different datasets are computed from subspaces that are affected differently by the closest modes. In this circumstance, the global mode shape vectors formed by merging all the mode shape parts might not yield well-defined global shapes. In other to overpass this issue, another strategy is proposed in this paper to force the mode shape parts corresponding to each dataset to be in the same subspace, and thus, yield global mode shapes with clearer configuration. The efficiency of the proposed strategy is demonstrated using an application example in the final part of the paper.
Period13 May 201915 May 2019
Event title8th International Operational Modal Analysis Conference
Event typeConference
Conference number8
LocationCopenhagen, Denmark
Degree of RecognitionInternational

Keywords

  • Modal Identification
  • Frequency Domain Decomposition
  • Multi-dataset Identification
  • Global Mode Shapes