Morlet-wave-based modal identification in the time domain

Ivan Tomac (Author), Janko Slavič (Author)

Abstract

This research focuses on the time-domain identification of modal parameters using impact response excitation from signals with a relatively small dynamic range and high noise contamination (e.g., from high-speed cameras). The information required to identify the modal parameters is limited and is contained mostly at the beginning of the signal. In order to perform an identification from such a response, the following challenges have to be overcome: a good frequency-domain separation (for close modes), a good localization in the time domain and an over-determination (to reduce uncertainty). To overcome these challenges this research introduces the Morlet-wave modal identification method as an extension of the Morlet-wave damping identification method, which has already proven capable of identifying the damping of short signals. Here, the method is extended to the modal parameters and an over-determination approach is proposed to reduce the uncertainty. The method identifies each mode shape separately from 10 to a maximum of 400 oscillations and at damping levels from 0.02% to 2% with a strong presence of noise in the signal. The method is tested on an experimental example and the results are compared to the classical modal identification methodology.

Keywords

Morletov valček;modalna identifikacija;modalni parametri;predoločen sistem;šum;Morlet-wave;modal identification;modal parameters;over-determination;noise;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	519.677:510.643
COBISS:	144036355
ISSN:	1096-1216
Views:	108
Downloads:	23
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	Morletov valček;modalna identifikacija;modalni parametri;predoločen sistem;šum;
Type (COBISS):	Article
Pages:	str. 1-11
Issue:	ǂVol. ǂ192
Chronology:	Jun. 2023
DOI:	10.1016/j.ymssp.2023.110243
ID:	18177061