# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
###### Abstract
proposed a new maml cross domain fault diagnosis method to improve the performance of the model by adding data from other working conditions or other  
Figure 1: Maml quadratic gradient iteration process. datasets [29]. [PERSON] et al. proposed a new conditional auxiliary classifier GAN framework with the MAML meta learning method, which has good performance for finite datasets [30]. [PERSON] et al. used triplet loss function to model the details of input pictures and learned a better classification method for input pictures by comparing the distance measurement difference between the same type of pictures and different types of data pictures [31]. As shown in Figure 3, the triplet neural network is mainly used to train samples with small differences. [PERSON] et al. proposed a countermeasure domain adaptive method based on triplet loss function guidance for bearing fault diagnosis [32]. [PERSON] et al. proposed a triplet triple metric driven multi head graph neural network to enhance the identification of unknown signals through decoupling confrontation learning [33]. For classification of small sample rolling bearing fault datasets, [PERSON] et al. propose a coupling vibration data classification method based on triplet embedding [34]. Both the maml meta learning method and triplet depth metric learning method show good results for small sample learning. We consider that the maml meta learning method can quickly create new classification tasks, but it may not be able to obtain the optimal results. Triplet depth metric learning has a good classification effect on small sample data, but it needs to judge difficult triples in the training process, resulting in the problem of a long training time. We created the maml triplet learning method, drawing on the rapid adaptability of the maml meta learning method and the excellent small sample classification effect of the triplet depth metric learning method. The specific improvement methods are as follows:  
1. The Maml-Triplet model for small sample fault diagnosis is proposed. An initial feature extractor that can be quickly adapted to multiple fault classification tasks is obtained using the Maml meta learning method, then the feature vector of signal is obtained using triplet depth measurement learning, and the fault type of unknown signal is judged by comparing the Euclidean distance of the feature vectors between different signals. 2. In the experiment, two datasets for training the Maml neural network are constructed using three-way five-shot fault classification tasks and three-way ten-shot fault clas  
Figure 3: Learning process of the triplet neural network. Figure 2: Initial weights for multitasking. sification tasks. Moreover, two different maml-triplet models are constructed based on two different Maml models. Finally, it is proved through experiments that the results of the two Maml-Triplet models are better than those of the other models with different numbers of datasets. 3. The influence of the number of fault types and the number of signal data in a single fault on the accuracy of the model is studied. Experiments show that, the more fault types, the lower the accuracy; the less signal data in a single fault, the lower the accuracy. In the experiment, it is also found that the number of fault types and the number of signal data within a single fault have less influence on the Maml-Triplet model than other models.