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Operational modal analysis (OMA) has attracted a lot of interest in the field of civil engineering during the past 15 years, to monitor structural health of large-scale infrastructure. Traditional contact-based modal analysis techniques require physically attached sensors for data collection and vibration-based monitoring which can impose mass-loading as well as financial constraints upon installation and maintenance of such devices. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using advanced computer vision techniques such as video motion magnification and optical flow have gained much importance. However, these techniques require prior information about the natural frequency ranges of the structure and utilize steerable pyramids which are complex multi-scale image decomposition filters. To address these issues, a technique is suggested in this study to extract the modal parameters (modal frequencies and mode shapes) blindly from the recorded structural vibration video signal using an unsupervised machine learning algorithm called Non-Negative Matrix Factorization (NNMF) integrated with a blind source separation technique called Generalized Complexity Pursuit (GCP). NNMF algorithm can be directly applied to the raw pixel-time series formed from the video data to obtain the temporal components, which can be demixed using GCP to identify the individual modal frequencies and mode shapes. The above algorithm is first validated on an 8-degree of freedom (DOF) numerical model and then implemented on laboratory-scale models (multi-storey shear frame model) as well as on real-world recorded structural vibration video like the Tacoma Narrows bridge to determine its noise sensitivity. The modal parameters extracted are compared with those from available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring, in identifying and reproducing even close-spaced as well as mildly excited modes of vibration.

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Blind Modal Parameter Identification Using Non-Negative Matrix Factorization and Generalized Complexity Pursuit Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Subhajit Banerjee,
  • T. Jothi Saravanan

摘要

Operational modal analysis (OMA) has attracted a lot of interest in the field of civil engineering during the past 15 years, to monitor structural health of large-scale infrastructure. Traditional contact-based modal analysis techniques require physically attached sensors for data collection and vibration-based monitoring which can impose mass-loading as well as financial constraints upon installation and maintenance of such devices. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using advanced computer vision techniques such as video motion magnification and optical flow have gained much importance. However, these techniques require prior information about the natural frequency ranges of the structure and utilize steerable pyramids which are complex multi-scale image decomposition filters. To address these issues, a technique is suggested in this study to extract the modal parameters (modal frequencies and mode shapes) blindly from the recorded structural vibration video signal using an unsupervised machine learning algorithm called Non-Negative Matrix Factorization (NNMF) integrated with a blind source separation technique called Generalized Complexity Pursuit (GCP). NNMF algorithm can be directly applied to the raw pixel-time series formed from the video data to obtain the temporal components, which can be demixed using GCP to identify the individual modal frequencies and mode shapes. The above algorithm is first validated on an 8-degree of freedom (DOF) numerical model and then implemented on laboratory-scale models (multi-storey shear frame model) as well as on real-world recorded structural vibration video like the Tacoma Narrows bridge to determine its noise sensitivity. The modal parameters extracted are compared with those from available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring, in identifying and reproducing even close-spaced as well as mildly excited modes of vibration.