多尺度代价敏感卷积神经网络的轴承故障诊断

针对滚动轴承故障诊断过程中因采集数据不平衡而导致诊断精度下降的问题开展研究。面向原始一维振动信号多尺度复杂性的特点，提出一种基于多尺度代价敏感卷积神经网络的不平衡故障诊断方法。构建串并联结构的多尺度一维卷积特征提取层，通过设计不同卷积层的连接方式和选取不同的卷积核大小实现多特征提取；利用注意力机制自适应设置Adacost代价敏感损失函数的代价矩阵，实现权重的自适应分配。通过在多种不平衡比率的西储大学轴承数据集上的实验表明：该方法能有效提升模型在不同不平衡数据集中的分类性能，且具有更强的泛化能力。

Bearing Fault Diagnosis Based on Multi-scale Cost Sensitive Convolutional Neural Network

Research was carried out on the problem of the decrease of diagnostic accuracy due to the imbalance of the collected data during the fault diagnosis of rolling bearings.Facing the characteristics of the multi-scale complexity of the original one-dimensional vibration signal,an imbalance fault diagnosis method based on multi-scale cost-sensitive convolutional neural network was proposed.A multi-scale one-dimensional convolution feature extraction layer with a series-parallel structure was constructed,and multi- feature extraction was achieved by designing the connection mode of different convolution layers and selecting different convolution kernel sizes; the attention mechanism was used to adaptively set cost matrix of the Adacost to realize the adaptive allocation of weights.Experiments on rolling bearing data sets of Western Reserve University with various imbalance ratios show that this method can effectively improve the classification performance of the model in different imbalance data sets,and has stronger generalization ability.