Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples.

Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.