基於多尺度熵分析法之生物啟發式結構健康診斷系統

Abstract

本研究提出一套以多尺度熵和多尺度交叉取樣熵為診斷邏輯的結構健康診斷系統。於生醫領域已成功運用人體的生理訊號，透過多尺度熵法的分析來辨別出健康狀態，本研究把微震動訊號視作結構物的生理訊號，以多尺熵(MSE)分析進行結構物之破壞程度的評斷，另外利用同一結構物不同樓層之訊號，進行交叉取樣熵分析法搭配多尺度(MSCE)的概念，可識別出結構物的破壞位置，並且提出以MSCE結果為基礎來計算的破壞指數，可有效且快速地指出破壞位置。不同於某些健康診斷系統，需要倚靠大量的資料庫或是數值模擬之結果，本系統僅需於破壞發生前量測一組初始值，另外也不需要長時間的量測，可降低整體成本。理論分析結果顯示，以結構物的微震動訊號進行多尺度熵分析，能夠分辦出不同的破壞程度，多尺度交叉取樣熵分析則能夠初步了解破壞位置，而破壞指數提供更快速且正確的破壞位置。最後於實驗場進行環境擾動試驗，同樣地以多尺度熵與交叉取樣熵分析法，能夠有效的區別結構物之破壞程度以及破壞位置，而破壞指數的結果，確實能夠提高破壞位置的診斷速度與準確性，更進一步驗證本研究之結構健康診斷系統於實際環境條件下應用的可行性。

A bio-inspired structural health monitoring (SHM) system based on multi-scale Entropy (MSE) and Multi-scale cross-Sample entropy (MSCE) is proposed in this paper. By measuring the ambient vibration signal from the structure, which is treated as the biological signal of the structure, the damage condition can be rapidly evaluated by the MSE analysis. The damage location can then be detected by analyzing the signals of different floors under the same damage condition with the MSCE analysis. Moreover, a damage index is proposed to efficiently quantify the SHM process. Unlike some existing SHM methods, no experimental database or numerical simulation is required. Simply a reference measurement of the current stage can initiate and launch the SHM system. Numerical simulation on a four-story steel structure has verified that the damage location and condition can be detected by the proposed SHM algorithm, and the damage location can be quantified by the damage index efficiently. Experimental verification was then conducted on a 7-story scale-down benchmark structure. Based on result, the damage condition can be correctly assessed, and an accuracy rate of 60% and 86% on the damage location can be achieved by the MSCE and damage index methods, respectively. As only the ambient vibration signal is required with a set of initial reference measured, the proposed SHM system can be implemented practically with low cost.