NDLI: Method of Laplacian Eigenmap-Based Pattern Recognition and Diagnosis for Incipient Fault of Pipelines

Content Provider	IEEE Xplore Digital Library
Author	Zhigang Lou Hongzhao Liu
Copyright Year	2011
Abstract	There is a considerable noise in the measured signal of pressure and flow of a running pipeline due to friction drag and medium diffusion, which poses an obstacle to the quick detection and precise classification of pipeline leakage, especially to the acquiring of weak incipient fault. This paper offers an incipient fault detection method based on nonlinear manifold learning algorithm, which treats the negative pressure wave signal as transient signal and reduces noise of original signal by using multi-scale wavelet transform. The method also learns original fault signal and extracts the intrinsic manifold features of data by using a nonlinear dimensionality reduction algorithm based on Laplacian Eigenmaps. With this method, the identification efficiency of optimal fault characteristics is noticeably improved, and the advantage of this method has been proved by simulation experiments.
Starting Page	66
Ending Page	69
File Size	521068
Page Count	4
File Format	PDF
ISBN	9781457715617
DOI	10.1109/ICICIS.2011.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-17
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Manifolds Laplace equations Wavelet transform Pipelines Noise Noise reduction Feature extraction Pattern recognition Pipeline fault diagnosis Laplacian Eigenmaps
Content Type	Text
Resource Type	Article

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