传感器信号融合的机械故障诊断

发布时间：2018-04-17 14:42

本文选题：传感器 + 信号采集　；参考：《激光杂志》2017年07期

【摘要】：为了提高机械故障诊断的正确率,针对当前机械故障诊断方法存在局限性,设计了基于传感器器信号融合的机械故障诊断方法。首先采用多个传感器对机械工作状态的信号进行采集,并从信号中抽取机械故障信息特征,然后采用主成分分析选择比较重要的机械故障信息特征,并采用支持向量机对机械故障信息特征向量进行学习,建立机械故障诊断的分类器,最后采用实际的机械故障数据进行了性能测试,结果表明,本文方法解决了当前机械故障诊断中的难题,获得了十分理想的机械故障诊断效果,而且机械故障诊断正确率要高于其它方法,具有广泛的应用前景。
[Abstract]:In order to improve the accuracy of mechanical fault diagnosis, a mechanical fault diagnosis method based on sensor signal fusion is designed to overcome the limitations of current mechanical fault diagnosis methods.Firstly, several sensors are used to collect the signals of the mechanical working state, and the mechanical fault information features are extracted from the signals, and then the principal component analysis (PCA) is used to select the more important mechanical fault information features.Support vector machine is used to study the feature vector of mechanical fault information, and a classifier for mechanical fault diagnosis is established. Finally, the performance test is carried out using actual mechanical fault data, and the results show that,In this paper, the problem of mechanical fault diagnosis is solved, and the result of mechanical fault diagnosis is very ideal. Moreover, the correct rate of mechanical fault diagnosis is higher than that of other methods, so it has a wide application prospect.
【作者单位】：沧州师范学院机械与电气工程学院;
【基金】：2015年河北省科技厅项目(15274514) 2015年河北省自然科学基金资助项目(B2015110004)
【分类号】：TH17;TP212

【相似文献】