Smart Structures and Systems

  • 제6권4호
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  • Pages.405-421
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  • 2010
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Early warning of hazard for pipelines by acoustic recognition using principal component analysis and one-class support vector machines

  • Wan, Chunfeng (International Institute for Urban Systems Engineering, School of Civil Engineering, Southeast University) ;
  • Mita, Akira (System Design Department, Keio University)
  • 투고 : 2009.07.29
  • 심사 : 2009.10.06
  • 발행 : 2010.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

키워드

참고문헌

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피인용 문헌

  1. Support vector machine for prediction of the compressive strength of no-slump concrete vol.11, pp.4, 2013, https://doi.org/10.12989/cac.2013.11.4.337
  2. Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers vol.50-51, 2015, https://doi.org/10.1016/j.ymssp.2014.05.032