海底管線缺陷內檢測技術與裝備工程化研究技術總結

張士華 黃松嶺 孫永泰 史永晉 王宏安

摘 要：漏磁檢測是在管道內檢測中應用最廣泛的一種無損檢測技術，檢測數據量化與分析是氣難點。在技術方面針對課題重點研究的關鍵技術開展了一系列研究，提出了油氣管道漏磁檢測數據的分類和量化方法，并基于此研發出一套漏磁檢測數據分析軟件。漏磁檢測中缺陷量化困難的原因在于缺陷的形態對漏磁場的形態有復雜的非線性的影響，繼而影響對漏磁信號的定量解釋，因此，根據缺陷的開口形狀將缺陷進行分類，對于實現將其準確量化是十分必要的。再者，由于實際檢測條件的限制，往往只能通過空間離散的漏磁感應強度信號的一維分量推算缺陷的三維形態，這本身不適合使用精確的數學或者統計模型加以描述。使用神經網絡對缺陷進行量化，是漏磁檢測缺陷量化領域近20年來的一個研究熱點。根據課題研究內容以及檢測器設計指標，提出了一種基于改進徑向基函數網絡的量化算法，它以缺陷漏磁場信號的特征量為輸入，輸出向量為缺陷的三維外形參數。徑向基函數網絡是一種局部最佳逼近網絡，但漏磁檢測中漏磁感應強度信號與缺陷外形之間強烈的非線性關系，往往更要求所選用的網絡能夠識別兩者間的內在聯系，并使得面對新的數據時仍有合理的量化結果。為此，對徑向基函數網絡做出基于泛化能力優化的改進，提出新的評價函數，并采用能夠迅速適應新樣本的在線學習算法，實驗驗證表明，的確能大幅提高網絡的泛化能力。在實際工程檢測管道中，多缺陷聚集會明顯影響漏磁場的形態，軸向槽缺陷漏磁場與兩個坑狀缺陷信號波形極為相似，緩變缺陷漏磁場信號變化趨勢較小，這對定量漏磁檢測的實用化是不容忽視的問題。討論了不同類型缺陷漏磁場形態和強度的影響，并測試了量化神經網絡對缺陷間隔變化的適應能力。研究以分類和量化算法為核心，研發一套漏磁檢測數據分析系統。該系統配合內檢測器已項目中投入測試，對牽拉實驗數據分析的結果驗證了所提出算法的確具有優秀的量化性能。

關鍵詞：漏磁檢測 缺陷分類 缺陷量化 多缺陷聚集 數據分析系統

Abstract：The magnetic flux leakage（MFL） is the most generalized method for in-pipe inspection. A method of classification and quantification of defects in MFL inspection is proposed， and a data analysis system is developed based on this method. The pattern of magnetic flux leakage has a complex non-linear relationship with the shape of defects， which makes it a difficult problem to make quantitative analysis to the magnetic flux leaked.Furthermore， in reality testing conditions， usually only the component in one direction is detected for quantification. Such problems do not adapt to accurate mathematical models. Utilizing neural network as a quantification method has become a focus in MFL inspection during the last 20 years. A method of quantification based on modified radial base function neural network （RBFNN） is proposed. RBFNN promises locally optimal approximation， but the non-linear relationship between magnetic flux pattern and the defect shape requires a strong capability to recognize their inner connection， to better deal with generalized samples.Anon-line trainingalgorithm to determine the number of nodes in hidden layer is proposed， and new merit function based on optimized generalization is employed to train the central vectors and widths. Both of them， verified by experiments， can greatly enhanced the generalized capability of RBFNN. Corrosions usually appear as multi-defect assemblies in pipelines. The relationship between magnetic flux leakage and the pattern of multi-defect assembly is discussed. And different neural network models are employed to solve the inverse problem for multi-defect assembly. Based on the methods stated above， a data analysis expert system is developed. This system works coordinating with in-line inspector and is tested in a submerged pipeline in-service testing project. Results prove that the modified methods gives accurate predicts to a wide range of defects.

Key Words：Magnetic Flux Leakage Inspection；Classification of Defects；Quantification of Defects；Multi-defect Assembly；Data Analysis System

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