基于连续高斯密度混合HMM的滚动轴承故障诊断研究

Continuous Gaussian Mixture HMM-based Diagnosing Method of Roller Bearing

滚动轴承在直升机的传动系统中占有十分重要的地位,对其进行快速有效的状态监测与故障诊断具有重大意义。由故障诊断和隐马尔可夫模型(HiddenMarkovModel,HMM)本质上的相通性,利用连续高斯密度混合隐马尔可夫模型分析滚动轴承的振动信号,先以基于短时傅里叶变换的倒谱系数为特征训练模型,再利用模型进行状态监测和故障诊断,实验结果表明该方法能利用少量样本进行训练和有效诊断,且具有训练时间短、诊断速度快的优点。

The roller bearings are very important to the gearing system of a helicopter, so it's necessary to monitor and diagnose their conditions and faults. Because condition monitoring and fault diagnosis are similar to Hidden Markov Model(HMM) in nature, four-state continuous Gaussian mixture HMM(Hidden Markov Model) is adopted to monitor and diagnose the roller bearings conditions and faults, which is trained through the features of Cepstrum Coefficient based on Short time Fourier transform extracted from vibration signals. The result shows that this proposal method can be used to diagnose rapidly with high correctness through small training samples.