Evidence map›Paper›PMID 35808420›Full record

ArticleSensors (Basel, Switzerland)2022

A Novel Method for Detecting Ferromagnetic Wear Debris with High Flow Velocity.

Feng Wang, Zhijian Liu, Xiaojing Ren, Sen Wu, Meilin Meng, Yulin Wang, Xinxiang Pan

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Feng WangCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Zhijian LiuCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Xiaojing RenCollege of Foreign Languages, Dalian Maritime University, Dalian 116026, China.
Sen WuCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.ORCID 0000-0002-9297-2346
Meilin MengCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Yulin WangCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Xinxiang PanCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, China.ORCID 0000-0002-0251-5679

Funding

National Natural Science Foundation of China 51909019
6 · The paper itself

Abstract

Inductance detection is an important method for detecting wear debris in ship lubricating oil. Presently, an LCR (inductance, resistance, capacitance) meter is generally used to detect wear debris by measuring the inductance change of the sensing coil. When ferromagnetic debris passes through the sensing coil, a pulse will appear in the inductance signal. Previous studies have shown that the amplitude of the inductance pulse decreases significantly with the increase in the particles' velocity. Therefore, it is difficult to detect ferromagnetic debris with a high flow velocity using an LCR meter. In this paper, a novel method, high-frequency voltage acquisition (HFVA), is proposed to detect ferromagnetic debris. Different from previous methods, the wear debris was detected directly by measuring the voltage change of the sensing coil, while the synchronized sampling method was utilized to ensure the higher-frequency acquisition of the sensor output signal. The experimental results show that when the velocity of particles increased from 6 mm/s to 62 mm/s, the amplitude of the signal pulse obtained by HFVA decreased by only 13%, which was much lower than the 85% obtained by utilizing the LCR method.

Indexed as

high-frequency voltage acquisition (HFVA)inductance detectionparticles velocitywear debris

Identifiers

PMID35808420
PMCPMC9269402

What OpenQuestion holds

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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.