Displacement monitoring has become an effective approach for assessing structural safety in civil engineering. Among structural components, bridge bearings are particularly prone to damage, drawing increasing research attention. Non-contact displacement monitoring offers significant advantages, including quick setup, essential in the confined workspaces typical of bridge inspections. This study applies image rotation-based displacement monitoring to 50 bridge bearings, constructing a dataset to analyze waveform patterns and their relationship to the safety status of the bearings, beyond just focusing on maximum amplitudes. This approach aims to streamline damage detection without the need for prior structural analysis of each bridge. To identify meaningful waveform patterns, the Consensus Motif method is employed to detect recurring structures in time series data, specifically using the Ostinato algorithm for motif searching. The algorithm processes displacement data collected from the 50 bridge bearings under traffic loads, clustering waveforms to distinguish damaged bearings from undamaged ones. The clustering results reveal a natural separation between these categories, highlighting the potential for enhanced damage detection. As the dataset expands, this method is expected to further improve accuracy, offering a more efficient and scalable solution for bridge bearing safety assessment.