Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Abstract Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. Although existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the “message-passing” procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

[1]  Bart Peeters,et al.  POLYMAX: A REVOLUTION IN OPERATIONAL MODAL ANALYSIS , 2005 .

[2]  Hubert W. Schreier,et al.  Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications , 2009 .

[3]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[4]  John E. Mottershead,et al.  Finite Element Model Updating in Structural Dynamics , 1995 .

[5]  Carlo Rainieri,et al.  Automated output-only dynamic identification of civil engineering structures , 2010 .

[6]  Rune Brincker,et al.  Modal identification of output-only systems using frequency domain decomposition , 2001 .

[7]  Peter Avitabile,et al.  Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry , 2017 .

[8]  Maria Q. Feng,et al.  Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review , 2018 .

[9]  V. Yaghoubi,et al.  Automated modal parameter estimation using correlation analysis and bootstrap sampling , 2017, 1707.00849.

[10]  E. Parloo,et al.  AUTONOMOUS STRUCTURAL HEALTH MONITORING—PART I: MODAL PARAMETER ESTIMATION AND TRACKING , 2002 .

[11]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[12]  Guido De Roeck,et al.  Fully automated (operational) modal analysis , 2012 .

[13]  Janko Slavič,et al.  The subpixel resolution of optical-flow-based modal analysis , 2017 .

[14]  Gaëtan Kerschen,et al.  Output-only modal analysis using blind source separation techniques , 2007 .

[15]  Charles R. Farrar,et al.  Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures , 2018 .

[16]  James M. W. Brownjohn,et al.  Fuzzy Clustering of Stability Diagrams for Vibration-Based Structural Health Monitoring , 2008, Comput. Aided Civ. Infrastructure Eng..

[17]  Frédo Durand,et al.  Modal identification of simple structures with high-speed video using motion magnification , 2015 .

[18]  Dario Di Maio,et al.  Continuous Scan, a method for performing modal testing using meaningful measurement parameters; Part I , 2011 .

[19]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[20]  Yongchao Yang,et al.  Blind modal identification of output‐only structures in time‐domain based on complexity pursuit , 2013 .

[21]  Charles R. Farrar,et al.  Efficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging , 2018, Journal of Engineering Mechanics.

[22]  Charles R. Farrar,et al.  Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification , 2017 .

[23]  Sindre Aavik Schanke Operational Modal Analysis of Large Bridges , 2015 .

[24]  Peter Avitabile,et al.  Photogrammetry and optical methods in structural dynamics – A review , 2017 .

[25]  P. Andersen,et al.  Automated Frequency Domain Decomposition for Operational Modal Analysis , 2007 .

[26]  H. Van der Auweraer,et al.  Discriminating physical poles from mathematical poles in high order systems: use and automation of the stabilization diagram , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[27]  Filipe Magalhães,et al.  Online automatic identification of the modal parameters of a long span arch bridge , 2009 .

[28]  Guido De Roeck,et al.  REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS , 1999 .

[29]  Dmitri Tcherniak,et al.  Clustering Approaches to Automatic Modal Parameter Estimation , 2008 .

[30]  Elías López-Alba,et al.  High frequency mode shapes characterisation using Digital Image Correlation and phase-based motion magnification , 2018 .

[31]  Yongchao Yang,et al.  Output-only modal identification with limited sensors using sparse component analysis , 2013 .

[32]  C. Farrar,et al.  Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision , 2019, Structural Control and Health Monitoring.

[33]  Nadine Martin,et al.  An automatic approach towards modal parameter estimation for high-rise buildings of multicomponent signals under ambient excitations via filter-free Random Decrement Technique , 2016 .