提出基于模糊层次分析的舰船故障数据定位挖掘算法，以解决舰船故障数据定位挖掘问题。使用基于模糊层次分析的舰船系统状态量化方法，诊断舰船系统故障状态后，通过基于主元分析方法的故障数据降维方法，将故障数据以降维的模式约束在同一维度中，提高故障数据可操行性。将降维后舰船故障数据，输入深度学习网络实现故障数据类型识别，从而完成故障数据定位挖掘。实验结果显示，所提算法应用后，降维后舰船故障数据维度一致，舰船故障状态识别准确，且故障数据定位挖掘结果符合实际，满足舰船系统运维需求，可用于舰船故障数据定位挖掘任务中。
An algorithm of ship fault data location and mining based on fuzzy analytic hierarchy process is proposed to solve the problem of ship fault data location and mining. After the fault state of the ship system is diagnosed by using the quantitative method of the ship system state based on fuzzy analytic hierarchy process, the dimension of the fault data is constrained in the same dimension by the dimension reduction method of the fault data based on the principal component analysis method, so as to improve the operability of the fault data. After the dimensionality reduction, the ship fault data is input into the deep learning network to realize the fault data type identification, so as to complete the fault data location mining. The experimental results show that after the application of the proposed algorithm, the dimensions of ship fault data are consistent after dimensionality reduction, the recognition of ship fault state is accurate, and the fault data location mining results are in line with the reality. The application performance can meet the needs of ship system operation and maintenance, and can be used in the task of ship fault data location mining.
2022,44(15): 149-152 收稿日期：2022-03-10
DOI：10.3404/j.issn.1672-7649.2022.15.031
分类号：TP621.3
作者简介：李川(1982-)，男，博士研究生，高级实验师，主要研究方向为复杂网络及数据挖掘
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