基于最佳小波包分解和HMM的齿轮故障模式识别

齿轮故障模式识别的关键问题在于对故障振动信号的特征提取。为了快速准确识别齿轮故障模式,提出了一种基于最佳小波包分解(OWPD)和隐马尔可夫模型(HMM)的识别方法。该方法对采集的振动信号进行小波包分解,再利用OWPD自动选择提取最佳小波包能量构造特征向量,输入HMM中进行训练与测试,实现了齿轮故障模式识别。实验结果表明该方法在齿轮故障模式识别方面的有效性和准确性。

Gear Fault Pattern Recognition based on the Optimum Wavelet Packet Decomposition and HMM

The key point of gear fault pattern recognition is the fault feature extraction of vibration signal. Aiming at feature extraction of gear fault pattern recognition,a method based on the Optimum Wavelet Packet Decomposition（ OWPD） and Hidden Markov Model（ HMM） is proposed in this paper. Processing of the vibration signals in the time domain is considered,using the wavelet packet. The characteristic energy automatically selected by OWPD is then employed as the input of HMM model for training and test. Finally the effect and accurate of the new method is validated by experiments.