Evidence map›Paper›PMID 39943552›Full record

ArticleSensors (Basel, Switzerland)2025

Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network.

Weixiong Jiang, Guanhui Zhao, Zhan Gao, Yuanhang Wang, Jun Wu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

5 authors.

Weixiong JiangSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0009-0004-6031-7875
Guanhui ZhaoCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China.
Zhan GaoSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Yuanhang WangSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China.
Jun WuSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0002-8657-5475

Funding

National Natural Science Foundation of China 523B2100
6 · The paper itself

Abstract

Wind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein, a compound fault diagnosis method based on modified signal quality coefficient (MSQC) and versatile residual shrinkage network (VRSN) is proposed to resolve these issues. In detail, the MSQC is designed to remove the noise components irrelevant to wind turbine operation status, and it has the ability to balance the denoised effect and signal fidelity. The VRSN is constructed for compound fault diagnosis, and it consists of two heterogeneous residual shrinkage networks. The former is designed to count the number of faults, and the latter is adopted to identify the single or compound fault pattern. Finally, a self-built wind turbine gearbox compound fault test rig is adopted to verify the proposed method's effectiveness. The results demonstrate that the proposed method is competitive in terms of compound fault diagnosis accuracy.

Indexed as

compound fault diagnosismodified signal quality coefficientversatile residual shrinkage networkwind turbine gearbox

Identifiers

PMID39943552
PMCPMC11820561

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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.