基于关联规则的舰船故障数据定位挖掘算法

发布时间：2018-05-27 13:18

本文选题：关联规则 + 舰船　；参考：《舰船科学技术》2017年24期

【摘要】：为了提高舰船故障检测能力,需要进行舰船故障数据的实时挖掘和分类分析,提出一种基于关联规则的舰船故障数据的定位挖掘方法。采用电磁探测器、水声换能器、声呐装置、声学传感器等设备进行不同工况下舰船数据采集,包括舰船辐射噪声、机械振动等数据,对采集的数据进行高维特征融合处理,提取舰船故障数据的关联规则特征量,对提取的特征量采用K均值算法进行聚类分析,并通过BP神经网络分类器实现舰船故障数据的分类识别,实现舰船故障数据定位挖掘。仿真结果表明,采用该方法进行舰船故障数据挖掘的准确性较好,对故障的定位能力较强,提高了舰船实时故障诊断能力。
[Abstract]:In order to improve the capability of ship fault detection, it is necessary to mine and classify ship fault data in real time, and a location mining method of ship fault data based on association rules is proposed. Using electromagnetic detector, underwater transducer, sonar device, acoustic sensor and other equipment to collect ship data under different working conditions, including ship radiation noise, mechanical vibration and other data, the data collected are processed with high dimensional feature fusion. The feature quantity of association rules is extracted from ship fault data. K-means algorithm is used to cluster the extracted feature quantity. The classification and recognition of ship fault data are realized by BP neural network classifier, and the location mining of ship fault data is realized. The simulation results show that the method is accurate and has a strong ability to locate faults and improve the ability of real-time fault diagnosis.
【作者单位】：钦州学院;
【基金】：基金项目钦州市“互联网+先进制造”工程技术研究中心(钦科发[2016]138号)
【分类号】：TP311.13;U674.707

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