含材料變異自由樑在頻率量測誤差下之多裂縫診斷

Multiple Cracks Identification of Free-Free Beam with Uniform Material Property Variation and Noised Frequency Measurement

林仁正(Ren-Jeng Lin)

指導教授 ： 鄭復平

國立交通大學/工學院/土木工程系所/博士(2009年)

https://doi.org/10.6842/NCTU.2009.00439

摘要

各種結構破壞診斷方法中，常採用有效的振動訊號來進行，但這些方法中甚少考慮材料變異因素，而這些變異可來自許多實務上的原因，也將影響振動訊號之內涵，進而誤導診斷及監測結果。 本研究提出一種破壞診斷統計模型，並以自由樑裂縫診斷例子驗證模型成效，其中模態曲率用來偵測裂縫位置，並以蒙地卡羅方法建立合適統計資料庫，再以含有量測誤差之實測模態頻率比對此統計資料庫，以獲得裂縫深度。研究中各測試例子之模態參數取自實際加工之鋼樑以及有限元素模擬分析結果，從各演算例子的結果可證明，本研究提出之結構破壞診斷統計模型，可具體說明在結構系統中楊式係數及質量密度同時變異條件下，引入量測模態頻率誤差情況，可有效進行多裂縫自由樑之裂縫數目及其位置之診斷。

關鍵字

破壞診斷 ； 多裂縫 ； 樑 ； 變異 ； 蒙地卡羅

並列摘要

It is common to apply damage-sensitive features from vibration response for structural damage assessment. Fewer damage identification algorithms have been taken into account the material variation. The material variation could be caused by many reasons in engineering practice, also there may exists certain level noise in measurement, these variations may affect the features used for structure monitoring and lead to an inaccurate damage assessment.

In this research the authors proposed a model to assess statistical structural damage of free-free beam structure. The modal curvature-base feature was used to identify crack location. The statistical database for damage severity assessment was build by applying the Monte Carlo simulation with Latin hypercube sampling. By mapping vibration-sensitive features with noised modal frequency to statistical damage database, the damage probability among various crack depths were then estimated; its statistical significance of damage level were examined by the t-test.

Data from simulated beams and experimental modal analysis were used to demonstrate the assessment procedures. From the results, the authors concluded that the proposed algorithm was robust and able to identify the damage of free-free beam under uniform mass density and stiffness variations incorporated with noise in measured frequency.

並列關鍵字

damage assessment ； multiple cracks ； beam ； variation ； Monte Carlo

參考文獻

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