广义隐马尔科夫模型及其在切削颤振识别中的应用-机械电子工程专业论文.docxVIP

华中 科 技 华 中 科 技 大 学 硕 士 学 位 论 文 II II Abstract In engineering practice, two types of uncertainty are ubiquitous when modeling and observing the physical processes. In most cases, traditional methods can only characterize variability and randomness. However, quantify variability and epistemic uncertainty separately can increase the reliability of observation and modeling. In this thesis, the Generalized Hidden Markov Model (GHMM) is proposed based on the Generalized Interval Probability theory and the Hidden Markov Model (HMM) algorithms. Furthermore, the toolbox of the model is established and then tested in the prediction of temperature rise of feed system bearing. A new approach in recognizing cutting states based on Generalized Hidden Markov Model is proposed. First, research on the uncertainty theory and applications of HMM is carried out. As uncertainty is ubiquitous in states recognition and prediction, the basic algorithms of GHMM is proposed based on the Generalized Interval Probability theory and HMM to solve the problem. Matlab toolbox of the GHMM is established based on the basic algorithms of GHMM. A further study in prediction of bearing temperature rise by using the GHMM toolbox is accomplished which verifies the effectiveness and feasibility of the algorithm and the toolbox. A milling experiment under variable conditions is carried out and the vibration signal of the milling spindle obtained during the experiment is used to identify the milling state, using the wavelet packet energy as the feature vector. The result shows that the Generalized Hidden Markov Model is efficient in cutting state recognition under small sample data, which forms the foundation of the prediction of cutting state transformation. Keywords: Uncertainty, Generalized Interval Probability, Generalized Hidden Markov Model, Cutting Chatter PAGE PAGE IV 目 录 摘 要 I HYPERLINK \l _bookmark0 Abstract II HYPERLINK \l _bookmark1 1 绪 论 HYPERLINK \l _bookmark2 1.1 选题概述 (1) HYPERLINK \l