%0 Journal Article
%T 主分量分析和隐马尔科夫模型结合 的轴承监测诊断方法<br>Bearing Fault Detection and Diagnosis Method Based on Principal Component Analysis and Hidden Markov Model
%A 张西宁
%A 雷威
%A 李兵
%J 西安交通大学学报
%D 2017
%R 10.7652/xjtuxb201706001
%X 为了快速识别轴承的故障模式以及性能退化状态,提出了一种基于主分量分析和隐马尔科夫模型的轴承监测诊断方法。该方法首先提取了轴承振动信号的混合域故障特征集,使用主分量分析对混合域故障特征集降维,然后使用降维后的特征训练隐马尔科夫模型,最后用降维后的测试样本测试模型的性能,根据隐马尔科夫模型输出的对数似然概率,确定轴承故障模式以及轴承的性能退化状态。开展了不同状态滚动轴承振动测试实验,数据分析结果表明,提出的方法诊断准确率均能达到100％,相比基于补偿距离选择特征降维及隐马尔科夫模型诊断方法,最高将分类离散度提高123？？74％,并且在轴承的性能退化实验中,提出的方法能在故障早期给出故障预警,证明了该方法的有效性和准确性。<br>Aiming at accurately and rapidly recognizing bearing fault pattern and performance degradation, a bearing fault detection and diagnosis method based on principal component analysis and hidden Markov model is proposed. The mixed domain fault feature set of bearing vibration signal, which corresponds to different bearing conditions, is extracted with principal component analysis to reduce the dimension of the feature set, then hidden Markov models are trained with part of the reduced feature set. The performances of trained models are verified with the remaining parts in this feature set. The bearing fault patterns are recognized and bearing performance degradation is assessed by comparing the logarithmic likelihood probability value of the hidden Markov models. Experiments under different bearing conditions are carried out, vibration signals are collected, and the correct classification rate of the proposed method reaches 100％. Compared with the compensation distance evaluation based feature dimension reducing technique and hidden Markov model, the classification dispersion of the proposed method is increased by 123.74％. In the bearing performance degradation monitoring, the proposed method exhibits better effectiveness and accuracy for early bearing degradation warning
%K 混合域故障特征集
%K 主分量分析
%K 隐马尔科夫模型
%K 轴承监测诊断&lt
%K br&gt
%K mixed domain fault feature set
%K principal component analysis
%K hidden Markov model
%K bearing fault detection and diagnosis
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201706001