發(fā)布時間：2018-07-17 01:16

【摘要】：隨著航運(yùn)業(yè)的發(fā)展,船舶的數(shù)量和規(guī)模不斷擴(kuò)大,作為航行安全的重要保障,船舶故障診斷技術(shù)得到越來越多的重視。由于船舶設(shè)備種類繁多且參數(shù)復(fù)雜,導(dǎo)致船舶管理系統(tǒng)中采集的數(shù)據(jù)量龐大且維數(shù)較高,對故障診斷模塊的數(shù)據(jù)處理性能提出了挑戰(zhàn),如何有效地處理海量高維數(shù)據(jù)成為故障診斷過程中的研究重點(diǎn)。本文以某海事局船舶管理系統(tǒng)為背景,重點(diǎn)研究了高維數(shù)據(jù)聚類技術(shù),設(shè)計(jì)并實(shí)現(xiàn)了故障診斷模塊,主要研究內(nèi)容如下。本文在深入分析了高維數(shù)據(jù)聚類算法和傳統(tǒng)聚類算法的基礎(chǔ)上,設(shè)計(jì)了一個基于高維數(shù)據(jù)聚類算法的故障診斷框架,并詳細(xì)闡述了該框架中各個組成部分的功能。針對故障診斷中出現(xiàn)的高維數(shù)據(jù)及其噪聲信息,本文重點(diǎn)研究了基于正交非負(fù)矩陣分解的聚類算法和基于相似矩陣補(bǔ)全的集成聚類算法。為了降低高維數(shù)據(jù)的維數(shù),提出了一種基于正交非負(fù)矩陣分解的K-means聚類算法,該算法對原始數(shù)據(jù)進(jìn)行非負(fù)矩陣分解,并加入正交約束,保證低維特征的非負(fù)性,增加數(shù)據(jù)原型矩陣的正交性,降低了數(shù)據(jù)的維數(shù)特征,最后進(jìn)行K-means聚類并驗(yàn)證該算法的有效性。為了解決高維數(shù)據(jù)中存在大量噪聲的問題,提出了一種基于相似矩陣補(bǔ)全的聚類集成改進(jìn)算法。該算法利用正交非負(fù)矩陣算法生成基聚類,在此基礎(chǔ)上采用高維數(shù)據(jù)相似性度量函數(shù)Hsim構(gòu)造每個基聚類的相似性矩陣,然后采用增廣拉格朗日乘子法對相似性矩陣中缺失的元素進(jìn)行補(bǔ)全,最后采用性能優(yōu)越的譜聚類得到最終的數(shù)據(jù)劃分。本文的研究成果初步應(yīng)用于某海事局船舶管理系統(tǒng)中的故障診斷模塊,以高維數(shù)據(jù)聚類算法為基礎(chǔ),實(shí)現(xiàn)了系統(tǒng)的故障診斷模塊,取得了良好的應(yīng)用結(jié)果。
[Abstract]:With the development of shipping industry, the number and scale of ships are expanding. As an important guarantee of navigation safety, ship fault diagnosis technology has been paid more and more attention. Because of the variety of ship equipment and the complexity of parameters, the data collected in the ship management system is huge and the dimension is high, which challenges the data processing performance of the fault diagnosis module. How to deal with massive high-dimensional data effectively becomes the focus of fault diagnosis. In this paper, based on the ship management system of a maritime bureau, the high-dimensional data clustering technology is studied, and the fault diagnosis module is designed and implemented. The main research contents are as follows. Based on the deep analysis of high-dimensional data clustering algorithm and traditional clustering algorithm, a fault diagnosis framework based on high-dimensional data clustering algorithm is designed in this paper, and the functions of each component of the framework are described in detail. Aiming at the high dimensional data and its noise information in fault diagnosis, this paper focuses on the clustering algorithm based on orthogonal nonnegative matrix decomposition and the integrated clustering algorithm based on similarity matrix complement. In order to reduce the dimension of high-dimensional data, a K-means clustering algorithm based on orthogonal non-negative matrix decomposition is proposed. It increases the orthogonality of the data prototype matrix and reduces the dimension feature of the data. Finally, K-means clustering is carried out and the validity of the algorithm is verified. In order to solve the problem of large amount of noise in high dimensional data, an improved clustering algorithm based on complement of similarity matrix is proposed. The algorithm uses orthogonal nonnegative matrix algorithm to generate base clustering. On this basis, the similarity matrix of each base cluster is constructed by using the high-dimensional data similarity measure function Hsim. Then the elements missing in the similarity matrix are complemented by the augmented Lagrangian multiplier method, and the final data partition is obtained by using the superior spectral clustering method. The research results of this paper have been applied to the fault diagnosis module of a ship management system of a maritime bureau. Based on the high-dimensional data clustering algorithm, the fault diagnosis module of the system has been realized, and good application results have been obtained.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U672.74
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本文編號：2128384