CNN和D-S证据理论相结合的齿轮箱复合故障诊断研究

A Composite Fault Diagnosis Method of Gearbox Combining with Convolution Neural Network and D-S Evidence Theory

针对齿轮箱复合故障诊断问题,将深度卷积模型(CNN)和D-S证据理论相结合,对多传感器信息进行融合。首先,利用深度卷积模型对多个传感器信息进行自适应特征提取,经softmax进行初步分类。其次,将深度卷积模型的输出结果作为D-S证据理论的输入,计算出基本概率分配,根据Dempster合成法则进行决策融合。为验证此方法对齿轮箱复合故障诊断的有效性,使用BP神经网络与D-S证据理论模型作为对比,并对自适应提取的特征与人工特征进行了主成分分析(PCA)。实验结果表明,利用该方法对齿轮箱复合故障进行实验诊断,准确率达到84.58%。相比单一传感器,正确率提高了7.91%;相比BP神经网络与D-S证据理论模型,正确率提高了6.18%,验证了此方法的有效性。

For the problem of gearbox composite fault diagnosis,the multi-sensor information fusion is used based on the deep convolution neural network(CNN)and the D-S evidence theory method.First,the information features of multiple sensors are extracted adaptively based on the CNN model.The information is preliminarily classified based on softmax.Secondly,the output of the CNN model is used as the input of D-S evidence theory.After the basic probability distribution is calculated,decision fusion is made according to the Dempster synthesis rule.In order to validate the effectiveness of this method for composite fault diagnosis of gearbox,BP neural network and D-S evidence theory model are used as comparison.Principal component analysis(PCA)is carried out based on adaptive extraction of features and artificial features.Experiments show that the accuracy of the composite fault diagnosis of gearbox is 84.58%.Compared with single sensor,the accuracy is improved by 7.91%.Compared with BP neural network and D-S evidence theory model,the accuracy is improved by 6.18%.Thus this method is proved effective.