Intelligent diagnosis algorithms can monitor faults with industrial production of a timely manner via their powerful learning ability. Multi-sensor diagnosis systems can more comprehensively describe the state of equipment and avoid the influence of incorrect data acquisition locations, which is beneficial to fault diagnosis. The fusion of the original data is a difficult problem, and it is hard to express effective information via traditional algorithms. This paper presents an adaptive data fusion strategy based on deep learning called the convolutional neural network with atrous convolution for the adaptive fusion of multiple source data (FAC-CNN). Specifically, an adaptive-sized convolution kernel that matches the channel of data sources is constructed to capture multi-source data without tedious preprocessing, and the channel of data sources is not limited. The atrous convolution kernel is introduced to expand the field of view of the FAC-CNN and extracts fusion sequence features without repeated computation, resulting in improved stability. The 1D-CNN is added to extract features after atrous convolution. In addition, batch normalization optimizes the distribution of fusion data and the structure of the model. The parametric rectified linear unit activation function and global average pooling are also introduced to improve network performance. The proposed method is validated on an industrial fan system with non-manufacturing faults and a centrifugal pump. Compared with other fusion methods and diagnosis algorithms based on feature engineering, namely CNN, ANN, and SVM, the FAC-CNN model is found to exhibit superior performance.

智能诊断算法可以通过其强大的学习能力及时监控工业生产中的故障。多传感器诊断系统可以更全面地描述设备状态，避免错误的数据采集位置的影响，有利于故障诊断。原始数据的融合是一个难题，很难通过传统算法表达有效信息。本文提出了一种基于深度学习的自适应数据融合策略，称为具有无穷卷积的卷积神经网络，用于多源数据的自适应融合（FAC-CNN）。具体来说，一种与数据源通道匹配的自适应大小的卷积核被构建为无需多味的预处理即可捕获多源数据，数据源的通道不受限制。引入无规卷积核可扩展FAC-CNN的视野，无需重复计算即可提取融合序列特征，从而提高了稳定性。添加了1D-CNN以在无意识卷积后提取特征。此外，批归一化还优化了融合数据的分布和模型的结构。还引入了参数化整流线性单元激活函数和全局平均池，以提高网络性能。提出的方法在具有非制造故障和离心泵的工业风机系统上得到了验证。与基于特征工程的其他融合方法和诊断算法（如CNN，ANN和SVM）相比，FAC-CNN模型具有更好的性能。引入无规卷积核可扩展FAC-CNN的视野，无需重复计算即可提取融合序列特征，从而提高了稳定性。添加了1D-CNN以在无意识卷积后提取特征。此外，批量归一化可优化融合数据的分布和模型的结构。还引入了参数化整流线性单元激活函数和全局平均池，以提高网络性能。提出的方法在具有非制造故障和离心泵的工业风机系统上得到了验证。与基于特征工程的其他融合方法和诊断算法（如CNN，ANN和SVM）相比，FAC-CNN模型具有更好的性能。引入无规卷积核可扩展FAC-CNN的视野，无需重复计算即可提取融合序列特征，从而提高了稳定性。添加了1D-CNN以在无意识卷积后提取特征。此外，批量归一化可优化融合数据的分布和模型的结构。还引入了参数整流线性单元激活函数和全局平均池，以提高网络性能。提出的方法在具有非制造故障和离心泵的工业风机系统上得到了验证。与基于特征工程的其他融合方法和诊断算法（如CNN，ANN和SVM）相比，FAC-CNN模型具有更好的性能。