【作者】 田春花；

【导师】 柳亦兵；

【作者基本信息】 华北电力大学（北京） ， 控制理论与控制工程， 2007， 硕士

【摘要】 隐马尔科夫模型(Hidden Markov Model,简称作HMM)是一种新的模式识别技术,其基本方法是通过对训练信号进行特征值提取和标量量化,建立具有相应状态数和观测值数的隐Markov模型,然后利用该模型计算待诊断信号与训练信号的相似概率,根据相似概率的差异判断信号状态的变化,达到信号模式分类的目的。本文的主要研究工作是利用HMM进行机械设备运行状态识别和故障诊断,包括三项研究内容:1)在深入学习HMM理论基础上,探讨HMM的技术实现方法,编制Matlab环境下的HMM实现程序;2)研究振动信号特征提取方法,开发基于LabVIEW的希尔伯特变换和倒频谱分析程序;3)应用HMM对于汽轮机和齿轮等两种典型旋转机械设备运行状态进行分析识别。对于汽轮机设备,以其振动信号频谱中基频处的幅值作为HMM训练的特征值,建立汽轮机升负荷过程的HMM,进行设备状态变化分析;对于齿轮箱设备,建立运行过程振动信号的HMM模型,根据相似概率的变化识别齿根裂纹故障的生成及发展趋势,两个应用案例都给出满意的结果。更多还原

【Abstract】 Hidden Markov Model (HMM) is a new technique in pattern recognition. By extracting and vector quantizing the features of training signal, Hidden Markov Mode with according state number and observation number can be established, then the similarity probability of the unknown signal can be calculated through HMM. By comparing the similarity probability, pattern of the signal can be recognized. In this work, HMM is applied in the pattern recognition and fault diagnosis of mechanical equipment, The mean works include three parts: 1) The realization approach of HMM for fault diagnosis was discussed and the program of HMM was developed with Matlab; 2) The methods of feature extraction from vibration signal, especially cepstrum and envelop method, was studied, and according program was developed with LabVIEW; 3) Two typical rotating machine(turbine and gearbox) with different signal characteristics were used to improve the effectiveness of HMM for identification and classification of machine condition. For the turbine, the amplitude at the basic frequency is extracted as the features for HMM training, for the gear box, HMM is established to recognize the development of gear crack. The results of these two examples are all satisfied.更多还原