Abstract
Purpose
The present work proposes a new method to realize the intelligent condition monitoring and fault diagnosis of marine machinery.
Method
To realize feature extraction, the time averaging decomposition method (TAD) is proposed to extract features from vibration signal. And a feature selection method, confusion score feature selection (CSFS), is proposed in this paper.
Results
The simulation data and the experimental data were analyzed in this work. Several signal decomposing method is compared in this paper and TAD is performed better than other methods. And CSFS method has better performance than other feature selection methods compared in this paper. Besides, the CSFS method will not only improve the prediction accuracy but also reduce the classifier computing time.
Conclusion
The proposed method is experimentally validated with a marine blower fault experiment, which proves the effectiveness of this proposed method.
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Acknowledgements
This work supported by Research Program supported by the Science & Technology Commission of Shanghai Municipality (Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency) under Grant 20DZ2252300, China.
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Jiang, J., Hu, Y., Chen, Y. et al. Feature Extraction and Intelligent Fault Diagnosis of Marine Machinery. J. Vib. Eng. Technol. 12, 201–211 (2024). https://doi.org/10.1007/s42417-022-00837-w
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DOI: https://doi.org/10.1007/s42417-022-00837-w