ABSTRACT

This paper describes techniques for detecting bearing malfunction directly from the seismic response of the bridge using wavelet transform time-varying identification. Instantaneous frequency of continuous wavelet and detail components of discrete wavelet transform are utilized as structural features, and they characterize isolation bearing condition via statistical clustering technique. Accuracy and efficacy of the techniques were verified in numerical simulations using analytical and finite element models. The techniques were implemented on seismic records from long-term monitoring of multi-span continuous-girder isolated bridge. Results demonstrate that wavelet-based features can effectively characterize the condition of the isolation bearing directly from seismic responses of girder and piers.

摘要

本文介绍了使用小波变换时变识别直接从桥梁的地震响应中检测轴承故障的技术。利用连续小波的瞬时频率和离散小波变换的细节分量作为结构特征，通过统计聚类技术表征隔震承载状态。使用分析和有限元模型在数值模拟中验证了这些技术的准确性和有效性。该技术应用于多跨连续梁隔震桥长期监测的地震记录。结果表明，基于小波的特征可以直接从梁和桥墩的地震响应中有效地表征隔震支座的状况。