隐马尔可夫树模型在机械状态诊断中的应用

›› 2007, Vol. 43 ›› Issue (1): 219-224.

隐马尔可夫树模型在机械状态诊断中的应用

桂林;武小悦

国防科技大学信息系统与管理学院

发布日期:2007-01-15

MACHINE CONDITION DIAGNOSIS USING HIDDEN MARKOV TREE

GUI Lin;WU Xiaoyue

Shool of Information System and Management, National University of Denfence Technology

Published:2007-01-15

摘要/Abstract

摘要： 隐马尔可夫树(Hidden Markov tree，HMT)模型作为一种小波变换系数的统计模型，可以表示小波系数的统计相关性及非高斯性。由于离散小波变换(Discrete wavelet transform，DWT)不具有平移不变性，应用基于DWT的HMT模型进行机械状态诊断时容易出现误诊。为了获得平移不变性，提出一种基于二分树复小波变换(Dual-tree complex wavelet transform，DT CWT)的HMT模型。实例表明与使用基于DWT的HMT模型进行状态识别相比，使用基于DT CWT的HMT模型的状态识别率有显著提高。

关键词: 二分数复小波变换, 平移不变, 隐马尔可夫树, 状态诊断

Abstract: As a statistical model of wavelet coefficients, hidden Markov tree (HMT) can consider the statistical dependencies and non-Gaussian statistics of wavelet coefficients. Due to shift-variance of discrete wavelet transform (DWT), if DWT- based HMT model is used for machine condition diagnosis, it is likely to get incorrect results. To obtain shift-invariance, DT CWT-based (dual-tree complex wavelet transform) HMT model is developed. Experiments show that DT CWT-based HMT model can get much higher recognition rates in comparison with DWT-based HMT model.

Key words: Condition diagnosis, Dual-tree complex wavelet transform, Hidden Markov tree, Shift-invariance

中图分类号:

TH165

桂林;武小悦. 隐马尔可夫树模型在机械状态诊断中的应用[J]. , 2007, 43(1): 219-224.

GUI Lin;WU Xiaoyue. MACHINE CONDITION DIAGNOSIS USING HIDDEN MARKOV TREE[J]. , 2007, 43(1): 219-224.

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