With the development of artificial intelligence and its applications in the field of engineering, knowledge acquisition is an effective way to solve the condition identification problem. In this paper the idea of artificial intelligence was applied to the condition identification of ship equipment. Knowledge acquisition method of decision tree based on information entropy was used to acquire condition identification knowledge with monitor instances. Then C4.5 algorithm was used to measure the relative importance of monitoring attributes to the condition identification of faults. Finally, this paper established the knowledge acquisition model based on decision tree and monitor instances library. This model was verified to be efficient and in accordance with the practical condition after being applied to a sample example. Therefore, it provides effective method and technology support for knowledge acquisition of condition identification of ship equipment faults based on monitoring instances.
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