为能获取可以准确描述船舶运行状态的故障数据，提出海量船舶故障数据挖掘的模糊运算聚类算法。使用局部切空间排列算法从船舶运行数据提取船舶故障征兆变量，利用离散化算法完成离散化处理运用模糊运算聚类算法挖掘与各故障征兆变量离散化结果相匹配的船舶故障数据。实验结果表明，该方法具有较优良的船舶故障征兆变量降维效果，海量船舶故障数据挖掘性能较为理想。
In order to obtain the fault data that can accurately describe the ship operation status, a fuzzy operation clustering algorithm for massive ship fault data mining is proposed. The local tangent space permutation algorithm is used to extract the ship fault symptom variables from the ship operation data, the discretization algorithm is used to complete the discretization processing, and the fuzzy clustering algorithm is used to mine the ship fault data matching the discretization results of each fault symptom variable. The experimental results show that this method has a better effect of reducing the dimension of ship fault symptom variables, and the performance of massive ship fault data mining is ideal.
2022,44(13): 182-185 收稿日期：2022-01-16
DOI：10.3404/j.issn.1672-7649.2022.13.040
分类号：TP206.3
基金项目：邢台市科技局项目（2019ZC009）
作者简介：魏亚清(1982-),男,硕士,讲师,从事物联网技术、计算机网络及数据挖掘研究
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