Evidence map›Paper›PMID 33572195›Full record

ArticleSensors (Basel, Switzerland)2021

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm.

Salvador Martínez-Cruz, Juan P Amézquita-Sánchez, Gerardo I Pérez-Soto, Jesús R Rivera-Guillén, Luis A Morales-Hernández, Karla A Camarillo-Gómez

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Salvador Martínez-CruzFacultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico.ORCID 0000-0001-6119-9146
Juan P Amézquita-SánchezFacultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico.ORCID 0000-0002-9559-0220
Gerardo I Pérez-SotoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro, Querétaro 76010, Mexico.
Jesús R Rivera-GuillénFacultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico.ORCID 0000-0002-9141-3454
Luis A Morales-HernándezFacultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico.ORCID 0000-0002-6483-0543
Karla A Camarillo-GómezTecnológico Nacional de México/Instituto Tecnológico de Celaya, Celaya, Guanajuato 38010, Mexico.ORCID 0000-0001-6968-9772

Funding

Consejo Nacional de Ciencia y Tecnología, México 783323
6 · The paper itself

Abstract

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

Indexed as

2-DOF planar robotfinite element methodMUSIC algorithmnatural frequenciesspectral analysis

Identifiers

PMID33572195
PMCPMC7915276

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.