# Domain Generalization Model of Deep Convolutional Networks Based on SAND-Mask
## 1 Introduction  
In the industrial production of the actual scene, the machine ran 24 h a day, usually for the real-time monitoring of running status of the machine, and it is very important to guarantee that the normal operation of machine fault diagnosis is able to build machine operation data and the relationships between the running state for real-time monitoring of the running status of the machine, as these have important research value. Generally, training data and test data are assumed to have the same distribution in fault diagnosis. However, due to the unavailability of some working condition data and the different distributions of data in different working conditions, fault diagnosis in an unknown working condition with data that pose a challenge to existing fault diagnosis methods. [PERSON] [1] proposed an RNN-based health indicator HI (health indicator) by extracting features and then transferring them to RNN networks and calculating the health indicator HI to predict bearing RUL (Remaining Useful Life), and the effectiveness of this method was demonstrated by bearing experimental data. [PERSON] [2] proposed a 1D-CNN combined with LSTM networks for a bearing diagnosis model, which was had the best classification accuracy that reached 99.6% without any preprocessing of the data. [PERSON] [3] proposed a deep recurrent neural network (DRNN) model, which consists of a stack of hidden units and a deep recurrent neural network. The model consists of stacked hidden units and LSTM units, and experiments show that classification accuracy can reach 94.75%. [PERSON] [4] constructed a fault diagnosis model by combining a ResNet model, which directly analyzes and diagnoses raw unprocessed data, and experiments show that, compared with CNN,Zhao [5] used wavelet coefficients to improve diagnosis accuracy in order to improve the performance of the ResNet-based diagnostic model by weighing the wavelet coefficients on a planetary gearbox containing severe noise; this had higher accuracy compared to other deep learning algorithms. The data-driven deep learning fault diagnosis approach based on data makes the assumption that training data are equally distributed with test data, which is usually not valid in practical scenarios. The domain adaptive DA (Domain Adaptation) technique and domain generalization DG (Domain Generalization) technique in migration learning are the two most important methods for solving the domain offset problem, which has been used by research scholars in the field of fault diagnosis to solve the domain offset problem caused by a change in working conditions in recent years. Domain offset fault diagnosis in migration learning is roughly divided into three broad scenarios: similar working conditions across machines, different working conditions across machines, and different working conditions of the same machine, and the first two cases are not discussed here. For the different working conditions of the same machine, [PERSON] [6] proposed a generalization model by using domain extension and distance measurement methods, which can learn generalized features from antagonistic heterogeneous domains, and verified the validity of the method on two rotating machinery datasets. [PERSON] [7] proposed a deep semi-supervised domain comprehensive network for fault diagnosis of variable-speed rotating machinery, which can effectively extend the model to fault diagnosis tasks under invisible speed. Both of these studies are limited to the case of domain generalization caused by a single condition such as rotation speed, but in the actual scene, multiple conditions can cause changes in the same machine condition. At present, most research studies are conducted in a scenario using a single condition, leading to the change of working conditions, and the scenario of multiple conditions, leading to a change of working conditions that needs to be further explored:  
* Its operating modes are numerous, and the characteristics of the working condition of the similarities and differences between the data are examined.