导　　师： 樊可清

学科专业： 081104

授予学位： 硕士

作　　者： ;

机构地区： 五邑大学

摘　　要： 当机械设备或大型建筑出现或将要出现某种故障和损伤时,其内部结构,外部形状或运行状态必将会有异常的表现。对被监测对象表面的振动信号进行分析,控制以及参数识别对于机械设备和建筑结构的诊断和维护工作有着重要的实际工程意义。 机械设备的振动信号蕴含着丰富的设备运转信息,是判断故障的重要来源。但振动信号往往不是单一的,并且在测取振动信号时受到传感器安装位置,还有故障实际振动方向不确定性的影响,在测量信号中必然含有其它信号的干扰。另一方面,由于故障的诱发振动可能会导致新的振动的产生。上述原因导致了目前设备的检测与故障处理的准确性难以提高。独立分量分析为解决上述问题开辟了一条新的研究途径。 本论文研究了独立分量分析在振动信号识别中的应用,主要工作如下: /(1/)搜集、整理、总结了国内外独立分量分析方面的成果和进展,介绍了独立分量分析的基本理论,并着重讨论了几种常用的独立分量分析算法及其特点。 /(2/)通过研究基于峭度的快速定点算法,很好地分离了混合语音信号与振动信号; /(3/)对于实际采集信号中出现的超定问题,即观测信号的个数多于源信号个数的情形,本文在奇异值分解的基础上,不仅很好地确定了信号源的个数,且很好地解决了噪声问题。提出了基于矩阵联合对角化的预白化JADE算法,通过计算机实验证实了该算法对盲信号具有较好的分离作用。 最后对全文的工作做了总结并对下一步的工作进行了展望。 When the mechanism and architecture have some failures, the interior structure, exterior shape or work condition will have the abnormal representation. It is practically important for the diagnose and maintenance of mechanism and architecture that implement and abnormity detection of abnormal vibratory signals obtained from the surface of the objects. The mechanical device vibration signal contains the plenty operation of equipment information. It is the important origin of diagnosis. But the vibration signal is often not single, because it is influenced by installation position of sensor and uncertainty of the breakdown actual vibration direction when measuring the vibration signal. So other disturbance signals are included in the survey signal inevitably. On the other hand, the new vibration can be produced possibly because of the induction vibration of the breakdown. The above reason has caused the present equipment examination and the breakdown processing accuracy improved difficulty. The independent component analysis opens a new research way for solution above question. In this paper, the application of independent component analysis in vibration signal processing has been studied. The paper consists of following parts: First, the paper reviews systematically the present research situation of independent component analysis in the world. The basic principles and concepts of independent component analysis and some algorithms are introduced. Second, the vibration source recognition apply a function based on the foundation of the fast independent component analysis algorithm, It separated the mixed voice and the mixed vibration source well; Third, as to the number of the mixed signal more than the source, the Singular Value Decomposition is applied, the method solved the number of the source and the noise; the matrix joint-diagonalizing pre-whitening JADE algorithm has been introduced, and the performance of the algorithm has been illustrated by computation simulation experiment. The re

关 键 词： 独立分量分析 主成分分析 盲源分离 奇异值分解

领　　域： [机械工程]