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A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Abstract

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

Keywords:

Fault diagnosis, Marine blower, EEMD, Correlation coefficient, AR spectrum, BPNN

Details

Issue
Vol. 29 No. 2 (2022)
Section
Latest Articles
Published
08-08-2022
DOI:
https://doi.org/10.2478/pomr-2022-0019
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Guohua Yan

    Shanghai Maritime University, Shanghai, China
  • Yihuai Hu

    Shanghai Maritime University, Shanghai, China
  • Jiawei Jiang

    Shanghai Maritime University, Shanghai, China

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