Defect recognition by metal magnetic memory detection of pipelines based on the fuzzy kernel function SVM

Shiyou Xuebao/Acta Petrolei Sinica

Volumn 31, Issue 5, 2010, Pages

  Yi, Fang ^a   Li, Zhuxin ^b   Lü, Hongqing ^b   Ma, Weiping ^c  

^a POL Research Institute of PLA's Airforce   (China)

^b Logistical Engineering University of PLA   (China)

^c MILITARY TRANSPORTATION UNIVERSITY   (China)

Author keywords

Defect; Fuzzy kernel function; Metal magnetic memory; Pipeline; Recognition; Support Vector Machine (SVM)

Indexed keywords

DE-NOISING; DEFECT RECOGNITION; FEATURE VECTORS; FOURIER COEFFICIENTS; FUZZY DATA; FUZZY KERNEL; INPUT VECTOR; MACRODEFECTS; METAL MAGNETIC MEMORY; MICRO-DEFECTS; NEW APPROACHES; PEAK VALUES; POLYNOMIAL KERNELS; RECOGNITION; SUPPORT VECTOR MACHINE (SVM); WAVELET PACKET;

DEFECTS; FERROMAGNETIC MATERIALS; FOURIER ANALYSIS; FREQUENCY BANDS; MAGNETIC STORAGE; METALS; PIPELINES; SIGNAL DETECTION; STRESS CONCENTRATION; SUPPORT VECTOR MACHINES; VECTORS;

FEATURE EXTRACTION;

EID: 77958473034     PISSN: 02532697     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. Doubov A A, Kouleev V G. Inspection of welding defects with metal magnetic memory method[J]. Welded Pipe and Tube, 2008, 31(2):44-48.
2. Liang Zhifang, Li Wushen, Wang Yingna, et al. Zero value character of metal magnetic memory signal[J]. Journal of Tianjin University, 2006, 39(7):847-850.
3. Shi Guangren. Application of support vector machine to multi-geological-factor analysis[J]. Acta Petrolei Sinica, 2008, 29(2):195-198.
4. Cristianini N, Shawe-Taylor J. An introduction to support vector machines[M]. Beijing: China Machine Press, 2005:21-38.
5. Yang Lunbiao, Gao Yingyi. Fuzzy mathematic principle and application[M]. Guangzhou: Huanan Institute of Technology Press, 2000:59-68.
6. Zhang Jun, Wang Biao. Recognition of signals for stress concentration zone in metal magnetic memory tests[J]. Proceedings of the CSEE, 2008, 28(18):144-148.
7. Liang Zhifang, Wang Yingna, Li Wushen, et al. General character of metal magnetic memory signal in tension experiment[J]. Journal of Harbin Institute of Technology, 2009, 41(5):99-101.
8. Yi Fang, Li Zhuxin, Su Yi, et al. Denoising algorithm for metal magnetic memory signals of oil pipeline based on improved wavelet threshold[J]. Acta Petrolei Sinica, 2009, 30(1):141-144.