Principal component analysis of the cross-axis apparent mass nonlinearity during whole-body vibration

Ya Huang*, Neil Ferguson

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    94 Downloads (Pure)

    Abstract

    During whole-body vibration (WBV), dynamic forces measured at the excitation-subject interface in directions other than the excitation axis, i.e. cross-axis response, are analysed using the principal component analysis (PCA) and virtual coherence techniques. The study applied these operations to the inline and cross-axis forces measured with twelve semi- supine human subjects exposed to longitudinal horizontal nominally random vibration between 0.25 and 20 Hz at root mean square acceleration levels of 0.125 ms-2 and 1.0 ms-2. The source identification is realised by a reversed path, aiming to identify relative contributions and correlations between the forces in response to a single axis excitation. The inline longitudinal and the cross-axis vertical forces were found to be correlated to each other from a low (e.g. 1 to 3 Hz) to a medium frequency range (e.g. 10 to 15 Hz). Above this range, where the forces were much reduced, the two forces tended to be independent in their contribution to the overall response. The singular vectors and virtual coherences were able to establish the degree of correlation in each of the frequency band identified. A signal processing framework is then proposed to take into account cross-axis responses for human vibration.
    Original languageEnglish
    Article number107008
    Number of pages13
    JournalMechanical Systems and Signal Processing
    Volume146
    Early online date12 Jun 2020
    DOIs
    Publication statusPublished - 1 Jan 2021

    Keywords

    • Whole-body vibration
    • principal component analysis
    • singular value decomposition

    Fingerprint

    Dive into the research topics of 'Principal component analysis of the cross-axis apparent mass nonlinearity during whole-body vibration'. Together they form a unique fingerprint.

    Cite this