Evidence map›Paper›PMID 38339668›Full record

ArticleSensors (Basel, Switzerland)2024

An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction.

Fengxia He, Chuansheng Zheng, Chao Pang, Chengying Zhao, Mingyang Yang, Yunpeng Zhu, Zhong Luo, Haitao Luo, Lei Li, Haotian Jiang

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

10 authors.

Fengxia HeSchool of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China.ORCID 0000-0003-3931-3728
Chuansheng ZhengSchool of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China.
Chao PangSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Chengying ZhaoSchool of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China.
Mingyang YangSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Yunpeng ZhuSchool of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK.
Zhong LuoSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.ORCID 0000-0002-0701-6123
Haitao LuoKey Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.ORCID 0000-0001-8865-6466
Lei LiSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Haotian JiangSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Funding

Basic Research Project of Shenyang Institute of Automation CAS (2022JC2K04)Central Government Guides Local Fund for Scientific and Technological Development in Liaoning Province 2023JH6/100100008Independent Project of State Key Laboratory of Robotics 2022-Z01National Natural Science Foundation of China 51975567Natural Science Foundation of Liaoning Province 2023-BS-132Space Automation Technology Research Laboratory Development Fund SIA (2023-D01)
6 · The paper itself

Abstract

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum-maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.

Indexed as

bearing faultscomplex faultsfault diagnosisfault separationIES-CYCBD

Identifiers

PMID38339668
PMCPMC10857667

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.