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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: Hidden Markov tree
Dual-tree complex wavelet transform
Shift-invariance Condition diagnosis
CLC No: TH165
国家自然科学基金资助项目(70571083). Received 20060212, received in revised form 20061011 |