基于KNN分类算法的水下航行器声学故障检测识别研究  

Detection and Identification of Acoustic Fault of Underwater Navigational Object Based on KNN Classification Algorithm

作　　者：周军伟[1] 朱海潮[1] 章林柯[1] 

机构地区：[1]海军工程大学振动与噪声研究所,武汉430033

出　　处：《噪声与振动控制》2009年第2期59-61,102,共4页Noise and Vibration Control

基　　金：国家自然科学基金(50775218);国防科技预研基金(9140A10050506JB1113)

摘　　要：对水下航行器声学故障进行检测和分类研究。利用水下航行器在正常工况下采集的声学信号建立正常样本基准数据库,对水下航行器实时采集到的声学信号进行扫描分析,把偏离一定基准阈值的信号判定为声学故障,并予以分离。由水下航行器声学故障的历史数据构建声学故障特征分类数据库,用KNN算法实现上述已分离出的声学故障信号的模式识别。设计一个水池试验验证了文中所提出方法的可行性。In this paper, a method of detection and classification of acoustic fault of underwater navigational objects is proposed. Firstly, a benchmark acoustic database of the collected acoustic signals of underwater navigational object in normal working condition is constructed. Scan analysis of these acoustic signals is made, and the benchmark threshold value of the signals is determined. The signals deviating from the threshold are extracted as the acoustic faults. Secondly, the database of characteristic classification of the acoustic faults is established based on the analysis of the historical data of the acoustic faults of the underwater navigational objects. Then, based on the acoustic fault database, the faults modes are identified and classified using K-Nearest Neighbor （KNN） algorithm. Finally, the feasibility of this method is verified by a tank experiment.

关 键 词：振动与波 水下航行器 声学故障 离群值检测 KNN算法 模式识别 

分 类 号：TB533[理学—物理]