導波檢測系統對於管線檢測具有長距離檢測的特性且可檢測到無法直接接觸的管線；其中，T(0,1)扭矩模態導波因非頻散特性而被廣泛使用在煉油石化廠裡大量的管線上。為了使檢測者更快速的判讀常見管線特徵之回波訊號，本研究建立一套類神經網路診斷系統，用以分類和辨識管線上常見特徵訊號。實驗中，利用環狀陣列式的壓電探頭，於六吋標準實驗管線上激發出T(0,1)扭矩模態導波，針對法蘭、銲道、彎管和彎管上具有缺陷等特徵進行檢測並擷取這些訊號，擷取出來的訊號再以數值軟體進行處理，以符合類神經網路樣本資料庫所需之大小，並將這些管線特徵訊號分為黑色軸對稱訊號、紅色非軸對稱訊號與兩訊號相除之紅黑比。本文進一步以有限元素法分析出不同結構銲道訊號振幅大小，以及模擬管線上常見特徵，包含法蘭、銲道及彎管，模擬結果符合實驗管線之回波訊號。網路的訓練與驗證資料必須使用實際訊號作為參數，因模擬所得回波訊號過於理想，故使用實驗訊號作為網路的特徵參數。類神經網路的辨識方面，另取得工業中實際管線訊號，用來測試網路訓練結果，測試管件有兩根，其中一根具有柏油包覆層，會使得訊號衰減，另一根管線上包含乾淨彎管和具有缺陷的彎管。本研究利用兩類別辨識方法，在辨識具有包覆的實際測試管線中，於低頻部分可成功的將法蘭與銲道分類出來，而高頻部分因柏油包覆使得訊號衰減，導致法蘭與銲道訊號相近，故會產生誤判。彎管上有缺陷與否的辨識，因彎管上具有缺陷的回波訊號分為3個波峰與2個波峰訊號，研究中以此兩種情況分開訓練網路，3波峰的收斂結果較優於2波峰的訊號；而彎管上具有缺陷時，3波峰的訊號會因樣本限制造成誤判，而2波峰訊號可辨識出來。

Guided ultrasonic detection system has the ability to inspect long range and not accessible pipelines. Especially, the T(0,1) mode guided wave was used widely at the detection, because the property of non-dispersive. For rapidly judge common features on pipe, this thesis makes an artificial neural network diagnosis system to separate and recognize the signals on pipeline. In the experimental setup, the torsional mode signal are excited by using an array of transducers distributed around the circumference of the 6-inch standard pipe, and the reflected signals contain flange, weld, elbow, and defect on elbow. These features are extracted and have been further processed to limit the size of the neural network; then, the feature signal classify as axisymmetric called black, non-axisymmetric called red, and dividing between the two called R/B ratio. The research also uses finite element method to simulate the weld by building up different kind of profile to analyze its amplitude and simulate the flange, elbow, and defect on the elbow. Because the reflection waves of simulation are too idealize to be the network data, the training data and validation data are collected from the experimental wave. In the recognition of artificial neural network, the signals were getting from two pipes of industry. One has bitumen on it, which makes signals attenuation. The other has a clear elbow and a notch on elbow. The two-class recognition method successfully separates flange and weld in low frequency; but in high frequency, the weld signal amplitude is close to flange signal, because the signals decay when guided waves pass to bitumen, and this makes the judge become error. Furthermore, the network recognizes defects on elbow, where the signals have 3 peaks and 2 peaks when the elbow has defect on it. The training result shows that the 3 peaks have better convergent than the 2 peaks in the network. Finally, the developed method can recognize those defects on the elbow when the reflection signals have 2 peaks, and when reflection signals have 3 peaks, it could not make a good judge because the network limit by sample data.

中文摘要 i
英文摘要 ii
目錄 iv
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1前言 1
1.2研究動機與目的 2
1.3文獻回顧 3
1.3.1導波文獻回顧 3
1.3.2類神經網路文獻回顧 5
1.4 研究方法 7
第二章 基本理論 9
2.1導波於圓管中傳遞之波動方程式 9
2.1.1縱向模態 10
2.1.2扭矩模態 10
2.1.3撓曲模態 11
2.2頻散曲線 12
2.3波形結構 14
2.4類神經網路 15
2.4.1類神經網路系統理論與架構 15
2.4.2類神經網路的學習方式 16
2.4.3類神經網路的特色 18
2.4.3.1快速運算能力 18
2.4.3.2高記憶容量 18
2.4.3.3學習能力 19
2.4.3.4容錯能力 19
2.4.4類神經網路的應用 19
2.4.4.1依問題類型分類 19
2.4.4.2依應用領域分類 20
2.5倒傳遞類神經網路 21
2.5.1 BPNN架構與參數建立 21
2.5.2倒傳遞類神經網路學習演算法 23
2.6有限元素法 26
第三章 實驗架構與模擬分析 38
3.1導波法管線檢測系統 38
3.1.1導波法檢測儀器設備 38
3.1.2測試管線規格 40
3.1.3導波法回波訊號判讀 40
3.2模擬分析 43
3.2.1元素與網格模型 43
3.2.2激發訊號與後處理 44
3.2.3回波訊號擷取 45
3.3模擬與實驗結果討論 47
第四章 應用倒傳遞類神經網路於管線辨識 66
4.1特徵訊號前處理 66
4.2網路訓練結果討論 69
4.3辨識結果討論 70
第五章 結論與建議 88
5.1結論 88
5.2未來展望 90
參考文獻 91

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