机械工程学报
機械工程學報
궤계공정학보
CHINESE JOURNAL OF MECHANICAL ENGINEERING
2014年
20期
18-25
,共8页
孙洁娣%肖启阳%温江涛%王飞
孫潔娣%肖啟暘%溫江濤%王飛
손길제%초계양%온강도%왕비
管道微小泄漏识别%局域均值分解%包络谱熵%支持向量机
管道微小洩漏識彆%跼域均值分解%包絡譜熵%支持嚮量機
관도미소설루식별%국역균치분해%포락보적%지지향량궤
pipeline small leak recognition%local mean decomposition%envelope spectrum entropy%support vector machine(SVM) 1
针对管道泄漏信号的非平稳特征以及管道泄漏孔径大小难以识别的问题,提出一种基于局域均值分解包络谱熵及支持向量机的识别方法。该方法对管道泄漏信号进行局域均值分解,得到若干个瞬时频率具有物理意义的乘积函数(production Function, PF)分量;计算各PF分量的峭度值并据此选出包含主要泄漏信息的分量作为主PF分量,对这些分量进一步采用小波包分解能量法进行分析并重构;再对重构后的主PF分量进行希尔伯特变换求取包络谱,结合信息熵的概念提出包络谱熵并计算熵值;将归一化包络谱熵作为泄漏信号特征输入支持向量机分类器中,用以区分不同的泄漏孔径,完成对泄漏孔径的识别。通过试验采集大量的管道泄漏信号进行处理及分析,试验结果表明该方法能有效识别不同泄漏孔径类别。
針對管道洩漏信號的非平穩特徵以及管道洩漏孔徑大小難以識彆的問題,提齣一種基于跼域均值分解包絡譜熵及支持嚮量機的識彆方法。該方法對管道洩漏信號進行跼域均值分解,得到若榦箇瞬時頻率具有物理意義的乘積函數(production Function, PF)分量;計算各PF分量的峭度值併據此選齣包含主要洩漏信息的分量作為主PF分量,對這些分量進一步採用小波包分解能量法進行分析併重構;再對重構後的主PF分量進行希爾伯特變換求取包絡譜,結閤信息熵的概唸提齣包絡譜熵併計算熵值;將歸一化包絡譜熵作為洩漏信號特徵輸入支持嚮量機分類器中,用以區分不同的洩漏孔徑,完成對洩漏孔徑的識彆。通過試驗採集大量的管道洩漏信號進行處理及分析,試驗結果錶明該方法能有效識彆不同洩漏孔徑類彆。
침대관도설루신호적비평은특정이급관도설루공경대소난이식별적문제,제출일충기우국역균치분해포락보적급지지향량궤적식별방법。해방법대관도설루신호진행국역균치분해,득도약간개순시빈솔구유물리의의적승적함수(production Function, PF)분량;계산각PF분량적초도치병거차선출포함주요설루신식적분량작위주PF분량,대저사분량진일보채용소파포분해능량법진행분석병중구;재대중구후적주PF분량진행희이백특변환구취포락보,결합신식적적개념제출포락보적병계산적치;장귀일화포락보적작위설루신호특정수입지지향량궤분류기중,용이구분불동적설루공경,완성대설루공경적식별。통과시험채집대량적관도설루신호진행처리급분석,시험결과표명해방법능유효식별불동설루공경유별。
When small leak occurs in the natural gas pipeline, it is difficult to identify the leak scale and aperture. It is proposed a small leak aperture recognition method based on local mean decomposition(LMD) envelope spectrum entropy and SVM. The leakage signals are decomposed into a number of production functions(PFs) components which have physical significance instantaneous frequencies. And then calculate the PFs kurtosis values and according to this select the principal PF components which contain most of leakage information. Further the wavelet packet decomposition and band energy distribution method are used to analyze the principal PF components and then reconstruct them. The Hilbert transform is applied to these reconstructed principal PF components and the corresponding envelope spectrums are obtained. Combining the concept of information entropy, the envelope spectrum entropy is proposed and calculates the entropy values. The normalized envelope spectrum entropy as the leakage feature is input the support vector machine(SVM) and the leak aperture classification is accomplished. By analyzing the acquired pipeline leakage signals in the field experiments, the results show that this method can effectively identify the different leak apertures.