Automated Operational Modal Analysis Method:A Survey

doi:10.3901/JME.2023.13.089

Abstract

Abstract: Driven by the requirements of structural health monitoring for high efficiency and automation of modal analysis, the automated operational modal analysis (AOMA) has been the focused area in the field of structural dynamics over the past two decades. Automatically discriminating and eliminating the spurious modes while reserving real modes in the identified results plays a core role in the AOMA. The current AOMA methods are classified into three classes:①Clustering method. The clustering methods are used to automatically select the stable modes in the stabilization diagram as the real modes, which is the most widely-used method at present. ②Peak picking method. It automatically picks the peaks, which are generated from the real modes, of power spectral density function or its singular values. ③Deep learning method. The neural network is used to transform the operational modal analysis problem into object detection or time series analysis problem. The basic ideas, computational processes and the merits/demerits of three method classes are reviewed, and the applications of AOMA on commercial software and engineering structures are also introduced. Finally, the common issues and development trends of AOMA are presented.

Key words: operational modal analysis, automated identification, spurious mode, clustering, peak picking, deep learning

CLC Number:

TG156

KANG Jie, WANG Yin, LUO Jie, SUN Jiabao, ZENG Shuhong. Automated Operational Modal Analysis Method:A Survey[J]. Journal of Mechanical Engineering, 2023, 59(13): 89-109.

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