Intelligent Fault Diagnosis of Reciprocating Compressor Based on Attention Mechanism Assisted Convolutional Neural Network Via Vibration Signal Rearrangement-Reference-Cited by-同舟云学术

Intelligent Fault Diagnosis of Reciprocating Compressor Based on Attention Mechanism Assisted Convolutional Neural Network Via Vibration Signal Rearrangement

Published:2021-04-01 Issue: Volume: Page:
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Zhao Dongfang^ORCID,Liu Shulin,Zhang Hongli,Sun Xin,Wang Lu,Wei Yuan

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://link.springer.com/content/pdf/10.1007/s13369-021-05515-9.pdf

Reference37 articles.

1. Zhao, H.Y.; Wang, J.D.; Lee, J.: A compound interpolation envelope local mean decomposition and its application for fault diagnosis of reciprocating compressors. Mech. Syst. Signal Process. 110, 273–295 (2018)

2. Potocnik, P.: Semi-supervised vibration-based classification and condition monitoring of compressors. Mech. Syst. Signal Process. 93, 51–65 (2017)

3. Zhang, Y.; Ji, J.C.; Ma, B.: Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network. Measurement 156, 107619 (2020)

4. Duan, L.X.; Wang, X.D.; Xie, M.Y.; Yuan, Z.: Auxiliary-model-based domain adaptation for reciprocating compressor diagnosis under variable conditions. J. Intell. Fuzzy Syst. 34(6), 3595–3604 (2018)

5. Sim, H.Y.; Ramli, R.; Saifizul, A.; Song, M.F.: Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique. Measurement 152, 107315 (2020)

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