Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis.

Yu Wang, Lei Chen, Yang Liu, Lipeng Gao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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

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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy.Entropy (Basel, Switzerland) · 2021
Article

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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

4 authors.

Yu WangInstitute of Vibration Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China.ORCID 0000-0001-8484-7302

Lei ChenInstitute of Vibration Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China.

Yang LiuInstitute of Vibration Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China.

Lipeng GaoInstitute of Vibration Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

Indexed as

fault diagnosisfew-shot learningmeta-learningrotating machinery

Identifiers

PMID33672742

DOI10.3390/s21041483

PMCPMC7924639

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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.