基于HMM-SVM的混合故障诊断模型及应用

 针对直升机减速器故障诊断中机器学习方法存在的问题,根据隐马尔可夫模型(HMM)适合于处理连续动态信号与支持向量机(SVM)适合于模式分类的长处,提出了基于HMM-SVM的混合故障诊断模型。先通过小波包分析方法从减速箱振动信号中有效提取非平稳特征,训练HMM-SVM模型,再利用训练好的模型进行监测与诊断,实验结果表明该方法优于单纯的HMM或SVM诊断方法,能利用少量训练样本有效地完成直升机减速器的故障诊断。

HMM-SVM Based Mixed Diagnostic Model and Its Application

The gearboxes are very important to the transmission system of a helicopter, so it is necessary to monitor and diagnose their conditions and faults. Because of the merit of hidden Markov model (HMM) that has the ability to deal with continuous dynamic signals and the merit of support vector machine (SVM) with perfect classify ability, the HMM-SVM based diagnostic method is presented. With the features extracted from vibration signals by wavelet packet decomposition, the HMM-SVM diagnostic model is trained and used to monitor and diagnose the gearbox’s conditions and faults. The results show that this proposal method is better than HMM-based and SVM-based diagnosing methods in high diagnostic accuracy with small training samples.