# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
###### Abstract  
Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less fault data. Therefore, it is necessary to study small sample fault data. For the case of less fault data, the Maml-Triplet fault classification learning framework based on the combination of maml and the triplet neural network is proposed. In the framework of Maml-Triplet fault classification, firstly, an initial signal feature extractor is obtained using the Maml training method. Secondly, the feature vectors corresponding to signal data are obtained using depth distance measurement learning in the triplet neural network, and the fault type is judged based on the feature vectors of unknown signal. The results show that the accuracy of the Maml-Triplet model is 2% higher than that of the triplet model alone and 5% higher than that of the Maml-CNN meta learning method. When there are fewer data samples, the accuracy gap is more obvious. Therefore, in the case of less data, the Maml-Triplet model has an excellent fault identification ability. M 2022 2020 October  
proposed IDSCNN, based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory, for bearing fault diagnosis, whose input is an FFT feature of the vibration signals from two sensors [14]. [PERSON] et al. designed a deep neural network structure called convolutional bidirectional long short-term memory (CBLSTM) to process raw sensory data [17]. In order to solve various operating conditions in fault diagnosis, [PERSON] et al. proposed a fault diagnosis method based on migration learning [8]. [PERSON] et al. proposed a feature-based transfer neural network (FTNN) [22]. However, the following phenomena exist in the actual work of the equipment: first, the equipment is not allowed to fail, especially the key systems and key failures; second, most failures occur slowly and equipment failures may occur for months or years; and third, the working conditions of the mechanical system of the equipment are very complex and the production requirements are often changed. It is unrealistic to collect and mark sufficient training data. These phenomena lead to insufficient data for deep neural network learning in practical work. For small sample learning fault diagnosis technology, in addition to data enhancement technology, another promising method to alleviate the problem of limited data is a small amount or one-time learning method [10], which has been successfully applied to a variety of tasks, including a small amount of image recognition, path planning of artificial intelligence, and recent fault diagnosis [23]. The few shot learning model has a strong generalization ability for categories with only a few samples in the training process [24]. It includes the maml learning method, Siamese network [25], and triplet neural network. [PERSON] et al. proposed the maml meta training learning method. In addition to more effectively summarizing new tasks, maml can also learn the process of learning itself, that is, how to learn [26]. As shown in Figures 1 and 2, Maml uses quadratic gradient iteration to descend and find a group of weight parameters with higher sensitivity. For multiple tasks, it can achieve global optimization through a few gradient iterations. [PERSON] et al. created the mamb model according to the maml model, randomly selected some data from the training set to form multiple fault classification tasks for training the maml model, and fine-tuned the following model layers at a low learning rate to obtain the final mamb model [27]. [PERSON] et al. proposed a bearing fault diagnosis model based on the maml model for small sample learning. The experimental results show that the accuracy of the twin neural network is 25% higher than that of the twin neural network [28]. [PERSON] et al.