基于时延相关解调-隐马尔科夫模型的故障诊断与模式识别研究

Research of the Fault Diagnosis and Pattern Recognition based on Delayed Correlation-Envelope Technique and Hidden Markov Model

针对齿轮性能参数的退化特点,提出了一种时延相关解调(DCE)和隐马尔科夫模型(HMM)相结合的故障模式识别与分析方法。该方法对采集的振动信号进行自相关时延去噪,提取能量特征,分别使用正常状态、齿根裂纹和齿轮断齿的全过程数据训练HMM,建立性能评估模型,然后进行模式识别。最后,通过与标准HMM进行对比,验证了所提出的方法的可行性和有效性。

For the degradation characteristic of the gear performance parameter,a method of fault pattern recognition and analysis, which combines the Delayed Correlation-Envelope （DCE） with the Hidden Markov Model（HMM） is proposed. The DCE is used to extract features from vibration signals. The HMM is trained respectively by using the data under normal condition, gear root crack condition and gear root breaking condition. Moreover, the trained HMMs are used to pattern recognition and as- sessment model. Finally, by comparing with standard HMM, the experimental results show that the proposed methodology is feasible and effective.