RESUMEN
In the detection of slipping anomalies in viscoelastic sandwich cylindrical structures (VSCS), conventional methods may encounter challenges due to the extremely rare and weak nature of slipping signals. This study focuses on normal signals and introduces unsupervised graph representation learning (UGRL) with discriminative embedding similarity for VSCS's detection. UGRL involves data preprocessing, model embedding, and matrix reconstructing. Association graphs are constructed based on sample similarities for yielding adjacency and attribute matrices. Subsequently, the matrices undergo embedding and reconstruction via various network modules to enhance graph data characterization. Detection indicators are derived by calculating embedding similarities and reconstruction errors, and thresholds are constructed using these indicators to enable efficient anomaly detection. The experiments in VSCS slipping dataset effectively indicate the superiority of the proposed method.
RESUMEN
Data-driven intelligent diagnosis model plays a key role in the monitoring and maintenance of mechanical equipment. However, due to practical limitations, the fault data is difficult to obtain, which makes model training unsatisfactory and results in poor testing performance. Based on the characteristics of 1-D mechanical vibration signal, this paper proposes Supervised Data Augmentation (SDA) as a regularization method to provide more effective training samples, which includes Cut-Flip and Mix-Normal. Cut-Flip is used directly on the raw sample without parameter selection. Mix-Normal mixes the data and labels of a random sample with a random normal sample at a certain ratio. The proposed SDA is verified on two bearing datasets with some popular intelligent diagnosis networks. Besides, we also design a Batch Normalization CNN (BNCNN) to learn the small dataset. Results show that SDA can significantly improve the classification accuracy of BNCNN by 10%-30% under 1-8 samples of each class. The proposed method also shows a competitive performance with existing advanced methods. Finally, we further discuss each data augmentation method through a series of ablation experiments and summarize the advantages and disadvantages of the proposed SDA.