Abstract Labeling the observations collected from the deep manned submersible is time-consuming and difficult, and traditional water leakage detection method is supervised. The online and unsupervised water leakage detection methods are necessary but barely researched. In this paper, a multisensor fusion method based on the sparse representation is proposed, and the unsupervised Bayesian-based online diagnostic technique is adopted for water leakage detection in the deep manned submersible. (1) Construct a robust model: an improved sparse representation method is introduced for obtaining a coefficient matrix. (2) Obtain the variable relationship: a coefficient matrix fusion method using the correlation coefficients of the variables is developed. (3) Detect the water leakage: the online Bayesian change point detection method using the fusion data is attempted for water leakage detection. Finally, a numerical simulation and a real deep manned submersible are used to illustrate the power of the proposed method.

中文翻译：

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基于贝叶斯的深水载人潜水器漏水检测与新型多传感器融合方法

摘要 对载人深潜器的观测数据进行标注耗时且困难，传统漏水检测方法需要监督。在线和无监督的漏水检测方法是必要的，但很少被研究。本文提出了一种基于稀疏表示的多传感器融合方法，采用基于无监督贝叶斯的在线诊断技术进行深载人潜水器漏水检测。(1)构建鲁棒模型：引入改进的稀疏表示方法来获取系数矩阵。(2)获取变量关系：开发了一种利用变量相关系数的系数矩阵融合方法。(3)检测漏水：尝试使用融合数据的在线贝叶斯变化点检测方法进行漏水检测。最后，数值模拟和真实的深载人潜水器被用来说明所提出方法的威力。