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In the last few years, mechanical fault diagnosis (FD) based on deep learning has been systematically applied to various industrial fields. However, most methods rely on enough labeled data, but collecting enough sample of failure is timeconsuming and labor-intensive. It remains challenging to train a model for FD with numbered training data and to function correctly under intricate operating scenario. In response to this problem, the FD method based on Siamese neural network (SNN) has shown promising results in recent years, especially in the case of insufficient training samples; the fault diagnosis work has achieved good results and has been widely concerned by researchers at domestic and foreign. To better combine the SNN for fault diagnosis and give full play to the advantages of the SNN in fault diagnosis, this paper summarizes and analyzes the fault diagnosis methods based on SNN. Firstly, the basic framework of the SNN method is introduced, and the advantages and application scenarios of the framework are pointed out. Secondly, the existing fault diagnosis methods based on SNN are classified, including the principle of the technique, improved methods, and shortcomings. Finally, the FD methods based on SNN are summarized, and the development of the SNN in FD is in prospect.
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