A Novel Outlier Detection Model for Vibration Signals Using Transformer Networks

Outlier detection in vibration signals can play an important role in addressing the issue of structural or environmental changes during vibration testing. In this study, a transformer-based model for outlier detection is proposed. Unlike previous statistical and regression outlier detection methods, the proposed model can identify the outlier location in a high dimensional observation space using the self-attention mechanism. The location of outliers within the vibration observation is marked by a combination of a spatial label and a temporal label. The outlier detection performance of the model is verified by a numerical study of the plane wave and an experimental study of the vibrating plate. These two studies show that the proposed model has good label prediction accuracies (all above 85%) toward the outlier location within the plane wave and vibrating plate observations.