Peng, W.-L.; Li, H.-L.; Bian, G.-R., and Zhang, E., 2020. Fault diagnosis and maintenance decision method of marine transformer: A rough set theory based study. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 562–565. Coconut Creek (Florida), ISSN 0749-0208.

With the rapid development of information technology, the requirements for fault diagnosis and maintenance of marine transformers are higher and higher in China, which will directly affect the safety of a ship's power operation system. When a ship power system is running, the transformer will generate a large amount of measurement and control data, which will become an important way to identify the fault diagnosis of the transformer. Through rough set theory, the algorithm of the multivariate decision tree for transformer fault diagnosis can be formed, which will better diagnose and repair the transformer fault. Through the transformer fault decision table, the transformer fault information can be determined, which will be a concise and reasonable multivariable decision tree. This article mainly describes a fault diagnosis expert system based on rough set theory, which can better detect ship transformers. Finally, this article sets up the transformer fault diagnosis process.

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