摘要
基于支持向量机和隐马尔可夫模型的诊断,提出了一种新的轴承故障诊断方案。结合SVM的分类能力和HMM的动态时间序列处理能力,通过sigmod函数和高斯模型,将支持向量机的输出信号转化成后验概率的形式,再引入HMM模型隐状态的观测概率,通过AR参数建立诊断的特征向量,从而提高轴承故障诊断精度。该方案的实验数据是通过小波分析提取自轴承的高频共振振动信号。
This paper presents a new scheme of bearing fault diagnosis based on SVM and HMM. Combining the classifica- tion ability of SVM and the ability of HMM to distinguish dynamic time series, by means of the sigmoid function and Gaussian model, we translate the information output of SVM into the form of posterior probability, and then introduce it into the observation probability estimation of hidden states in HMM model. Feature vectors used in diagnosis are established by AR parameters to im- prove the accuracy of fault diagnosis of bearing. The scheme was tested with experimental data extracted from the high frequency resonant vibration signal of bearing by wawelet analysis.
出处
《武汉理工大学学报（信息与管理工程版）》
CAS
2016年第2期267-270,共4页
Journal of Wuhan University of Technology：Information & Management Engineering
基金
国家自然科学基金项目(61374151
61304181)
