Journal
STRUCTURAL CONTROL & HEALTH MONITORING
Volume 26, Issue 11, Pages -Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/stc.2436
Keywords
automated peak picking; convolutional neural network; deep learning; object detection; operational modal analysis
Funding
- Technology Advancement Research Program (TARP) [19CTAP-C143249-02]
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Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses. One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region-based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy.
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