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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.