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首页> 外文期刊>Mechanical systems and signal processing >CNN-LSTM deep learning architecture for computer vision-based modal frequency detection
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CNN-LSTM deep learning architecture for computer vision-based modal frequency detection

机译：CNN-LSTM基于计算机视觉模型频率检测的深度学习架构

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摘要

The conventional modal analysis involves physically-attached wired or wireless sensors for vibration measurement of structures. However, this method has certain disadvantages, owing to the sensor's weight and its low spatial resolution, which limits the analysis precision or the high cost of optical vibration sensors. Besides, the sensor installation and calibration in itself is a time consuming and labor-intensive process. Non-contact computer vision-based vibration measurement techniques can address the shortcomings mentioned above. In this paper, we introduce CNN-LSTM (Convolutional Neural Network, Long Short-Term Memory) deep learning based approach that can serve as a backbone for computer vision-based vibration measurement techniques. The key idea is to use each pixel of an image taken from an off the shelf camera, encapsulating the Spatio-temporal information, like a sensor to capture the modal frequencies of a vibrating structure. Non-contact "pixel-sensor" does not alter the system's dynamics and is relatively low-cost, agile, and provides measurements with very high spatial resolution. Our computer vision-based deep learning model takes the video of a vibrating structure as input and outputs the fundamental modal frequencies. We demonstrate, using reliable empirical results, that "pixel-sensor" is more efficient, autonomous, and accurate. Robustness of the deep learning model has been put to the test by using specimens of a variety of materials, and varying dimensions and results have shown high levels of sensing accuracy.
机译：传统的模态分析涉及物理连接的有线或无线传感器，用于振动测量结构。然而，由于传感器的重量和其低空间分辨率，该方法具有一定的缺点，这限制了分析精度或光学振动传感器的高成本。此外，传感器安装和校准本身是耗时和劳动密集型的过程。基于非接触式计算机视觉的振动测量技术可以解决上述缺点。在本文中，我们介绍了基于CNN-LSTM（卷积神经网络，长短期记忆）深度学习的方法，可以用作基于计算机视觉振动测量技术的骨干。关键思想是使用从架子相机拍摄的图像的每个像素，封装时空信息，如传感器以捕获振动结构的模态频率。非接触式“像素传感器”不会改变系统的动态，并且相对低成本，敏捷，并提供非常高的空间分辨率的测量。我们的计算机视觉的深度学习模型将振动结构的视频作为输入，输出基本的模态频率。我们使用可靠的经验结果来证明“像素传感器”更有效，自主和准确。深入学习模型的鲁棒性已经通过使用各种材料的标本来进行测试，并且不同的尺寸和结果显示出高度的感测精度。

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  • 来源
    《Mechanical systems and signal processing》 |2020年第10期|106885.1-106885.18|共18页
  • 作者

    Ruoyu Yang; Shubhendu Kumar Singh; Mostafa Tavakkoli; Nikta Amiri; Yongchao Yang; M. Amin Karami; Rahul Rai;

  • 作者单位

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

    Mechanical Engineering-Engineering Mechanics Michigan Technological University 1400 Townsend Drive Houghton MI 49931 USA;

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

    Department of Mechanical and Aerospace Engineering University at Buffalo 240 Bell Hall Buffalo NY 14260-4400 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    CNN (convolutional neural network); LSTM (long short-term memory); Computer vision; Modal analysis;

    机译：CNN（卷积神经网络）;LSTM（长期内存）;计算机视觉;模态分析;

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