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    Efficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging

    Source: Journal of Engineering Mechanics:;2018:;Volume ( 144 ):;issue: 007
    Author:
    Dorn Charles;Dasari Sudeep;Yang Yongchao;Farrar Charles;Kenyon Garrett;Welch Paul;Mascareñas David
    DOI: 10.1061/(ASCE)EM.1943-7889.0001449
    Publisher: American Society of Civil Engineers
    Abstract: Traditional vibration measurement typically requires physically attached sensors, such as accelerometers and strain gauges. However, these discrete-point sensors provide only low spatial resolution vibration measurements, potentially foregoing valuable structural information such as localized damage. Noncontact optical measurement methods such as laser vibrometers can achieve high spatial resolution vibration measurements but only through time-consuming sequential measurements and are not cost-effective. As an alternative to traditional vibration measurement methods, digital video cameras are relatively low-cost, agile, and offer noncontact, simultaneous high spatial resolution measurements where every pixel on the structure becomes a measurement point. However, regular digital video cameras are frame-based where each pixel simultaneously performs temporally uniform (synchronous) measurements containing large amounts of redundant (background) data, which consumes considerable resources for video data measurement, management, and processing. To alleviate such a challenge, this work explores the use of event-based neuromorphic imagers, specifically silicon retinas, an efficient alternative to traditional frame-based video cameras, to perform full-field vibration measurements and operational modal analysis. By imitating biological vision, each silicon retina pixel independently and asynchronously records only intensity change events that contain structural motion information while excluding redundant (background) information. Such an asynchronous event-based data measurement mechanism allows for structural motion to be captured on the microsecond scale in an extremely data-efficient manner, which could benefit real-time vibration measurement and control applications. This study takes the first step toward these applications by formulating an existing video frame-based full-field operational modal analysis technique in the event-based, asynchronous silicon retina measurement framework. Specifically, local phase-based motion extraction and blind source separation are used to automatically and efficiently extract full-field vibration and dynamics parameters from silicon retina measurements. The developed method is validated by laboratory experiments on a bench-scale cantilever beam.
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      Efficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging

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    contributor authorDorn Charles;Dasari Sudeep;Yang Yongchao;Farrar Charles;Kenyon Garrett;Welch Paul;Mascareñas David
    date accessioned2019-02-26T07:41:33Z
    date available2019-02-26T07:41:33Z
    date issued2018
    identifier other%28ASCE%29EM.1943-7889.0001449.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248756
    description abstractTraditional vibration measurement typically requires physically attached sensors, such as accelerometers and strain gauges. However, these discrete-point sensors provide only low spatial resolution vibration measurements, potentially foregoing valuable structural information such as localized damage. Noncontact optical measurement methods such as laser vibrometers can achieve high spatial resolution vibration measurements but only through time-consuming sequential measurements and are not cost-effective. As an alternative to traditional vibration measurement methods, digital video cameras are relatively low-cost, agile, and offer noncontact, simultaneous high spatial resolution measurements where every pixel on the structure becomes a measurement point. However, regular digital video cameras are frame-based where each pixel simultaneously performs temporally uniform (synchronous) measurements containing large amounts of redundant (background) data, which consumes considerable resources for video data measurement, management, and processing. To alleviate such a challenge, this work explores the use of event-based neuromorphic imagers, specifically silicon retinas, an efficient alternative to traditional frame-based video cameras, to perform full-field vibration measurements and operational modal analysis. By imitating biological vision, each silicon retina pixel independently and asynchronously records only intensity change events that contain structural motion information while excluding redundant (background) information. Such an asynchronous event-based data measurement mechanism allows for structural motion to be captured on the microsecond scale in an extremely data-efficient manner, which could benefit real-time vibration measurement and control applications. This study takes the first step toward these applications by formulating an existing video frame-based full-field operational modal analysis technique in the event-based, asynchronous silicon retina measurement framework. Specifically, local phase-based motion extraction and blind source separation are used to automatically and efficiently extract full-field vibration and dynamics parameters from silicon retina measurements. The developed method is validated by laboratory experiments on a bench-scale cantilever beam.
    publisherAmerican Society of Civil Engineers
    titleEfficient Full-Field Vibration Measurements and Operational Modal Analysis Using Neuromorphic Event-Based Imaging
    typeJournal Paper
    journal volume144
    journal issue7
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/(ASCE)EM.1943-7889.0001449
    page4018054
    treeJournal of Engineering Mechanics:;2018:;Volume ( 144 ):;issue: 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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