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

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...

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

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.