Evidence map›Paper›PMID 36832644›Full record

ArticleEntropy (Basel, Switzerland)2023

A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD.

Jingzong Yang, Chengjiang Zhou, Xuefeng Li, Anning Pan, Tianqing Yang

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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.

Jingzong YangSchool of Dig Data, Baoshan University, Baoshan 678000, China.ORCID 0000-0002-2211-8718
Chengjiang ZhouSchool of Information, Yunnan Normal University, Kunming 650500, China.ORCID 0000-0003-4218-105X
Xuefeng LiCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Anning PanSchool of Dig Data, Baoshan University, Baoshan 678000, China.
Tianqing YangSchool of Dig Data, Baoshan University, Baoshan 678000, China.

Funding

10th batches of Baoshan young and middle-aged leaders training project in academic and technical 202109PhD research startup foundation of Yunnan Normal University 01000205020503131scientific research fund project of Baoshan University ZKMS202101Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities' Association 202001BA070001-109
6 · The paper itself

Abstract

In modern industry, due to the poor working environment and the complex working conditions of mechanical equipment, the characteristics of the impact signals caused by faults are often submerged in strong background signals and noises. Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method.

Indexed as

CYCBDfault feature extractionrolling bearingTVDVMD

Identifiers

PMID36832644
PMCPMC9955811

What OpenQuestion holds

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LicenceCC BY
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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.