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| Research on Marine Equipment Fault Diagnosis Based on Decision Tree Support Vector Machine Algorithm |
| Received:June 28, 2021 Revised:July 10, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2021.09.011 |
| KeyWord:support vector machine fault diagnosis decision tree intelligent engine room |
| Author | Institution |
| TANG Qi-guan |
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai , China |
| CHE Chi-dong |
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| In order to improve the intelligent level of marine equipment and enhance the safety and reliability of ships, it is urgent to monitor the status of the equipment on the ship, and evaluate the health status of the equipment based on the monitoring data to identify possible fault conditions. The vibration data collected in the cabin was preprocessed using fast Fourier transform to extract the one-third octave band features, and the octave band spectrum signal was used as the feature vector. Support vector machine algorithm was used for model training and classification. For a variety of working conditions on the ship and possible multiple fault categories, the decision binary tree method was used to propose a fast and accurate state monitoring and fault diagnosis strategy. The identification accuracy under laboratory conditions was close to 100%. This method can provide support for state monitoring, fault diagnosis and health assessment of marine equipment, and provide basis for decision-making of equipment maintenance and sensor arrangement. |
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