<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals"}

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,...

A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Publié en ligne: 05 août 2022

Pages: 77 - 86

DOI: https://doi.org/10.2478/pomr-2022-0019

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

© 2022 Guohua Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Publié en ligne: 05 août 2022

Pages: 77 - 86

DOI: https://doi.org/10.2478/pomr-2022-0019

Mots clésFault diagnosis, Marine blower, EEMD, Correlation coefficient, AR spectrum, BPNN

© 2022 Guohua Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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