Evidence map›Paper›PMID 37299789›Full record

ArticleSensors (Basel, Switzerland)2023

Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection.

Jian Liu, Jiaqi Guo, Bing Hu, Qiqing Zhai, Can Tang, Wanjia Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

6 authors.

Jian LiuCollege of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.ORCID 0000-0002-6548-3002
Jiaqi GuoCollege of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.
Bing HuCollege of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Qiqing ZhaiCollege of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Can TangCollege of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.
Wanjia ZhangCollege of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.

Funding

National Natural Science Foundation of China 62001210,62201284
6 · The paper itself

Abstract

Weak fault detection with stochastic resonance (SR) is distinct from conventional approaches in that it is a nonlinear optimal signal processing to transfer noise into the signal, resulting in a higher output SNR. Owing to this special characteristic of SR, this study develops a controlled symmetry with Woods-Saxon stochastic resonance (CSwWSSR) model based on the Woods-Saxon stochastic resonance (WSSR), where each parameter of the model may be modified to vary the potential structure. Then, the potential structure of the model is investigated in this paper, along with the mathematical analysis and experimental comparison to clarify the effect of each parameter on it. The CSwWSSR is a tri-stable stochastic resonance, but differs from others in that each of its three potential wells is controlled by different parameters. Moreover, the particle swarm optimization (PSO), which can quickly find the ideal parameter matching, is introduced to attain the optimal parameters of the CSwWSSR model. Fault diagnosis of simulation signals and bearings was carried out to confirm the viability of the proposed CSwWSSR model, and the results revealed that the CSwWSSR model is superior to its constituent models.

Indexed as

AlgorithmsVibrationComputer SimulationNoiseSignal Processing, Computer-Assistedcontrolled symmetry with Woods-Saxon stochastic resonance (CSwWSSR)particle swarm optimization (PSO)signal-to-noise ratio (SNR)weak fault detection

Identifiers

PMID37299789
PMCPMC10255917

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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.