基于非线性时序模型盲辨识的因子隐Markov模型识别方法

基于模型辨识的机械有效故障特征提取方法中输入信号难以确定,以及机械设备运行过程中具有信息量大、非平稳、特征重复再现性差的特点,结合非线性时序模型盲辨识和因子隐Markov模型,提出一种基于非线性时序模型盲辨识的特征提取的因子隐Markov模型识别方法,并应用到旋转机械升降速过程故障诊断中.同时还与基于Fourier变换、小波变换的特征提取的因子隐Markov模型识别方法进行比较,试验结果表明该方法是有效的.

FACTORIAL HIDDEN MARKOV MODEL RECOGNITION METHOD BASED ON BLIND DENTIFICATION OF NONLINEAR TIME SERIES MODELS

Considering the problem of hardly determinate input signals in machine fault diagnosis method based on the system identification,and the characteristics of the abundant informa-tion,non-stationary,poor repeatability and reproducibility in the operating process of the mechanical equipment,here,combined blind identification of nonlinear time model and factorial hid-den Markov Model(FHMM),a fault diagnosis approach named as BSI-FHMM,is proposed.This approach is that blind identi-fication of nonlinear time series model is used as a feature ex-traction,and FHMM as a classifier,the proposed approach has been successfully completed in the speed-up and speed-down process of rotating machinery.At the same time,this approach is compared with another two fault diagnosis approaches named FFT-FHMM,wavelet-FHMM respectively.In the FFT-FHMM and wavelet-FHMM recognition approaches,the Fourier trans-formation and wavelet transformation is used as a feature ex-traction respectively,FHMM as a classifier.Experimentresults show that the BSI-FHMM recognition approach is supe-rior to the FFT-FHMM and wavelet-FHMM recognition approaches.