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基于隐马尔科夫模型的齿轮故障诊断

发布时间:2018-06-05 07:32

  本文选题:齿轮故障诊断 + 隐马尔科夫模型 ; 参考:《南昌航空大学》2014年硕士论文


【摘要】:齿轮传动在机械设备中应用广泛,其运行状态的好坏直接决定了整个设备的性能,因此对齿轮运行状态进行在线监测和故障诊断尤为重要。齿轮传动是以轮齿的周期性啮合传递运动,这一过程必将产生机械振动,当齿轮出现制造装配误差、磨损、裂纹等故障与缺陷时,必然会使机械振动产生不同的变化,其啮合振动信号中包含了丰富的齿轮状态信息,因此,分析齿轮啮合振动信号是齿轮故障诊断最有效的方法。 在齿轮的使用中,希望能够及早的发现故障并对故障做出诊断,就可以合理的使用齿轮或制定维修计划,提高齿轮的利用率,防止生产事故的发生。为此,本文以齿轮为分析对象,,采用理论分析与实验研究相结合,深入研究了基于隐马尔科夫模型(HMM:Hidden Markov Model)的齿轮故障诊断方法与技术,主要做了以下几个方面的工作。 1.分析了齿轮故障诊断的研究意义,综述了齿轮故障诊断技术的发展与HMM在故障诊断中的应用,阐述了齿轮故障模式,常见故障的振动机理与齿轮啮合振动信号的特征。 2.研究了HMM的基本理论,重点讨论了连续隐马尔科夫模型(CHMM:Continuous HMM)的理论,针对算法下溢与模型参数的初始化这两个实际应用中出现的问题,给出了解决方案。最后给出基于HMM的齿轮故障诊断的思路与流程。 3.提出了基于细化谱分析的齿轮故障特征提取方法,并将其应用在离散隐马尔科夫模型(DHMM:Discrete HMM)中,该方法首先利用时域同步平均提取目标齿轮的振动信号,再进行细化谱分析提取主要频率及其附近的边频带幅值作为特征向量,量化后输入到模型中进行训练和分类。通过实验验证了该方法的有效性。 4.应用CHMM结合AR系数的特征提取方法,进行了齿轮故障诊断与齿轮的状态识别。在齿轮状态识别的研究中,进行了齿轮的全生命周期实验,采用交叉验证寻找最优状态数,并用K均值聚类算法对模型进行状态初始化,成功的对生命周期三个阶段进行了识别,为齿轮箱的状态监测提供了科学依据。
[Abstract]:Gear transmission is widely used in mechanical equipment, and its running state directly determines the performance of the whole equipment. Therefore, it is very important to monitor and diagnose the running state of gear on line. Gear transmission is transmitted by periodic meshing of gear teeth, which will produce mechanical vibration. When the gear has faults and defects such as assembly error, wear, crack and so on, it will inevitably cause different changes in mechanical vibration. The meshing vibration signal contains abundant information of gear state. Therefore, the analysis of gear meshing vibration signal is the most effective method for gear fault diagnosis. In the use of gears, it is hoped that the faults can be detected and diagnosed as soon as possible, so that the gears can be reasonably used or maintenance plans can be formulated, the utilization ratio of gears can be improved, and the occurrence of production accidents can be prevented. In this paper, the gear fault diagnosis method and technology based on Hidden Markov Model (hmm: Hidden Markov Model) are studied by combining theoretical analysis with experimental research. The main work is as follows. 1. The research significance of gear fault diagnosis is analyzed. The development of gear fault diagnosis technology and the application of HMM in fault diagnosis are summarized. The gear fault mode, the vibration mechanism of common faults and the characteristics of gear meshing vibration signal are expounded. 2. In this paper, the basic theory of HMM is studied, and the theory of continuous Hidden Markov Model (CHM: continuous HMMM) is discussed. The solutions to the problems of algorithm overflow and the initialization of model parameters are given. Finally, the train of thought and flow chart of gear fault diagnosis based on HMM are given. 3. A gear fault feature extraction method based on thinning spectrum analysis is proposed and applied to discrete Hidden Markov Model (DHMM: discrete HMMM). Firstly, the vibration signal of the target gear is extracted by time-domain synchronous averaging. Then the main frequency and the amplitude of the edge band are extracted as the eigenvector by the thinning spectrum analysis, and then the quantization is input into the model for training and classification. The effectiveness of the method is verified by experiments. 4. Gear fault diagnosis and gear state recognition are carried out by using CHMM and AR coefficient feature extraction method. In the research of gear state recognition, the whole life cycle experiment of gear is carried out, the optimal state number is found by cross validation, and the state of the model is initialized with K-means clustering algorithm. The three stages of the life cycle are identified successfully, which provides scientific basis for the condition monitoring of the gearbox.
【学位授予单位】:南昌航空大学
【学位级别】:硕士
【学位授予年份】:2014
【分类号】:TH132.41;TH165.3

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本文编号:1981176


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