2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

线性AR-HMM在旋转机械故障诊断中的应用

旋转机械的连续监测日益成为重要问题。由于传感技术的发展和计算能力的提高 ,使设备的在线监测与故障诊断成为可能。把线性AR系数作为暂态振动信号的特征 ,利用有限状态隐马尔可夫模型 (简称HMM )来模拟旋转机械的运行过程中动态行为的变化 ,从而提出了一种新的故障诊断方法。HMM的参数从监测数据的统计关系中导出。