Characterization of identification errors and uses in localization of poor modal correlation. (1st May 2017)

• Record Type: Journal Article
• Authors: Martin, Guillaume; Balmes, Etienne; Chancelier, Thierry
• Is Part Of: Mechanical systems and signal processing. Volume 88(2017)
• Page Start: 62
• Page End: 80
• Publication Date: 2017-05-01
• Subjects: Modal assurance criterion -- Identification error -- Localization of poor correlation; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.11.006
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: While modal identification is a mature subject, very few studies address the characterization of errors associated with components of a mode shape. This is particularly important in test/analysis correlation procedures, where the Modal Assurance Criterion is used to pair modes and to localize at which sensors discrepancies occur. Poor correlation is usually attributed to modeling errors, but clearly identification errors also occur. In particular with 3D Scanning Laser Doppler Vibrometer measurement, many transfer functions are measured. As a result individual validation of each measurement cannot be performed manually in a reasonable time frame and a notable fraction of measurements is expected to be fairly noisy leading to poor identification of the associated mode shape components. The paper first addresses measurements and introduces multiple criteria. The error measures the difference between test and synthesized transfer functions around each resonance and can be used to localize poorly identified modal components. For intermediate error values, diagnostic of the origin of the error is needed. The level evaluates the transfer function amplitude in the vicinity of a given mode and can be used to eliminate sensors with low responses. A Noise Over Signal indicator, product of error and level, is then shown to be relevant to detect poorly excited modes and errors due to modal property shifts between test batches. Finally, a contribution is introduced to evaluate the visibility of a mode in each transfer. Using tests on a drum brake component, these indicators are shown to provide relevant insight into the quality of measurements. In a second part, test/analysis correlation is addressed with a focus on the localization of sources of poor mode shape correlation. The MACCo algorithm, which sorts sensors by the impact of their removal on a MAC computation, is shown to be particularly relevant. Combined with the error it avoids keeping erroneous modal components. Applied after removal of poor modal components, it provides spatial maps of poor correlation, which help localizing mode shape correlation errors and thus prepare the selection of model changes in updating procedures. Abstract : Highlights: Identification error is decomposed in contributions per mode and sensor which can be used to localize experimental errors. Four indicators are used to assist the definition of sets of sensors by mode. The four indicators especially helpful for the analysis large datasets. The MACCo algorithm further helps detecting well identified areas where poor correlation occurs.