Ensemble All Time-Scale Decomposition Method and Its Application in Bevel Gear Fault Diagnosis.

Zhengyang Cheng, Yu Yang, Chengcheng Duan, Xin Kang, Jianxin Cui

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Ensemble All Time-Scale Decomposition Method and Its Application in Bevel Gear Fault Diagnosis.Sensors (Basel, Switzerland) · 2024
Article

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5 · Who and what money

Authors and funding

5 authors.

Zhengyang ChengCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

Yu YangCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

Chengcheng DuanCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

Xin KangCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

Jianxin CuiCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

Funding

National Natural Science Foundation of China 52275103

6 · The paper itself

Abstract

All time-scale decomposition (ATD) is a non-parametric adaptive signal decomposition method, which relies on zero-crossing points and extreme points to jointly construct the baseline, achieving the suppression of modal mixing caused by the proximity of component frequencies. However, ATD is unable to solve mode mixing induced by noise. To improve this defect, a new noise-assisted signal decomposition method named ensemble all time-scale decomposition (EATD) is proposed in this paper. EATD introduces the noise-assisted technique of complementary ensemble empirical mode decomposition based on ATD, adding complementary noises to mask the noise interference in the signal. EATD not only overcomes mode mixing caused by noise but also preserves the capability of ATD to suppress mode mixing caused by the proximity of component frequencies. Simulation signals and bevel gear fault signals are utilized to validate EATD, and the results indicate that EATD can successfully overcome mode mixing induced by noise and can be effectively applied for gear fault diagnosis.

Indexed as

all time-scale decompositionensemble all time-scale decompositiongear fault diagnosisnoise-assisted techniquezero-crossing point

Identifiers

PMID39796810

DOI10.3390/s25010023

PMCPMC11723328

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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.