Evidence map›Paper›PMID 36146267›Full record

ArticleSensors (Basel, Switzerland)2022

A Design of Electromagnetic Velocity Sensor with High Sensitivity Based on Dual-Magnet Structure.

Xiao Zhou, Yangfan Ruan, Xingang Mou, Yuhao Yuan, Yi He

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Xiao ZhouSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.ORCID 0000-0002-3591-0982
Yangfan RuanSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.ORCID 0000-0002-0209-2549
Xingang MouSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
Yuhao YuanSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
Yi HeIntelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China.

Funding

National innovation and entrepreneurship training program for college students 216
6 · The paper itself

Abstract

The most permanent magnets in current electromagnetic velocity sensors are magnet cylinders that have been axially magnetized, with magnetic boots changing the propagation direction of the magnetic induction lines of the magnet cylinders. However, the magnetic field generated by the magnet cylinders is not fully utilized, which leads to uneven magnetic field intensity of the working air-gap and high magnetic field intensity of the nonworking air-gap. We propose a novel dual-magnet structure (DM) mainly consisting of two magnet loops that are magnetized radially and a magnetic conductive shaft, adopting a concentric nested configuration. The dual-magnet structure can make the magnetic induction lines enter the working air-gap directly from the magnet and increase the effective magnetic field, which is perpendicular to the coils in the working air-gap. This design can further improve the sensitivity of a velocity sensor and enhance its ability to detect weak signals in microtremor exploration. The validity of the dual-magnet structure has been established by numerical simulations and verified by experiments. The results reveal that the magnetic field intensity is increased by 29.18% and the sensitivity is improved by 23.9%, when the total volume and material of the magnet are unchanged. The full utilization of the material is achieved without increasing the complexity of the structure.

Indexed as

dual magnetmagnetic field intensitysensitivityvelocity sensor

Identifiers

PMID36146267
PMCPMC9505507

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Registered trials

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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.