基于多块信息提取的AUV资源勘查系统故障检测

针对"潜龙二号"AUV在实际航行过程中,资源勘查系统传感器数据具有多重变量相关性、故障类型多样、受运行状态和环境变化影响数值变化大以及噪声强等问题,提出一种新的基于多块信息提取的主元分析(PCA)故障检测方法.首先,针对变量之间的多重相关性,通过滑窗和相关系数的方法提取变量间相关性信息;然后,根据变化率在不同运行状态和环境下基本稳定的特点,对于不同类型故障,分别提取变化率信息和变化率信息的各阶统计量累积误差信息;最后,基于提取的特征信息建立3个子块,对每个子块分别建立PCA模型并进行检测,将检测的结果通过中值滤波去噪后,用贝叶斯推断进行融合.通过对"潜龙二号"实际运行数据进行检测,验证所提方法的有效性.

Fault detection of AUV resource exploration system based on multi-block information extraction

Aiming at the problems of AUV of Qianlong 2 in the actual navigation process, such as multi-variable correlation, multiple fault types, large numerical variation influenced by operation status and environmental changes, and strong noise, a new principal component analysis (PCA) fault detection method based on block information extraction is proposed. Firstly, according to the multiple correlations among variables, the correlation information between variables is extracted by sliding window and correlation coefficient method. Secondly, according to the basic stable characteristics of change rate in different operating states and environments, for different types of faults, the cumulative error information of each order statistics of change rate information and change rate information is extracted separately. Finally, three sub-blocks are built based on the extracted feature information, and the PCA model is built and tested for each sub-block respectively. After de-noising by median filtering, the detected results are fused by Bayesian inference. The effectiveness of the proposed method is verified by testing the actual operation data of Qianlong 2.