基于Blending多卷积神经网络模型融合的滚动轴承声学故障诊断方法

Acoustic Fault Diagnosis of Rolling Bearings Based on Blending Model Fusion of Multiple Convolutional Neural Network

针对滚动轴承故障诊断中单一网络模型的不确定问题,并考虑到声信号非接触式测量的优势,提出一种多卷积神经网络(CNN)模型融合的滚动轴承声学故障诊断方法,采用多通道传声器信号对每一个CNN进行训练,然后采用Blending模型融合方法将多CNN模型进行融合,实现更精确、更可靠的故障诊断。通过半消声室内滚动轴承实验台的传声器数据对该方法的有效性进行实验验证。结果证明:与单个CNN模型、支持向量机(SVM)、随机森林法(RF)、多层感知机(MLP)等方法相比,该方法可以避免复杂的人工特征提取过程,通过模型融合能获得更高的诊断精度,并在一定程度上可以克服声学诊断中不易选择传声器测点位置的问题。

Aiming at the uncertainty of a single network model in the fault diagnosis of rolling bearings,and taking into account the advantages of non-contact measurement of production signals,a multi-reputation convolutional neural network(CNN)model fusion method for rolling bearing production and academic fault diagnosis is proposed.Employ multi-channel transmitter signal to train each CNN,and then uses the blending model fusion method to merge the multiple CNN models to achieve more accurate and reliable fault diagnosis.The effectiveness of the proposed method is experimentally verified with the transmitter data of the semi-consumable indoor rolling bearing test bench.Compared with other methods like the single CNN model,support vector machine(SVM),random forest method(RF),multi-layer perceptron(MLP),this method can avoid the complex manual feature extraction process,and render the higher diagnosis accuracy through model fusion furthermore,and to a certain extent,it can solve the problem that it is difficult to select the location of the transmitter in the acoustic diagnosis.