Premium
Automated peak picking using region‐based convolutional neural network for operational modal analysis
Author(s) -
Kim Hyunjun,
Sim SungHan
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2436
Subject(s) - convolutional neural network , computer science , artificial intelligence , detector , salient , pattern recognition (psychology) , representation (politics) , acceleration , artificial neural network , identification (biology) , noise (video) , algorithm , physics , telecommunications , botany , classical mechanics , politics , political science , law , image (mathematics) , biology
Summary 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.