Failure Mode Recognition Clustering Algorithm Based on Manifold Learning

Zhi Gang Lou; Hong Zhao Liu

doi:10.4028/www.scientific.net/AMM.263-266.2126

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266Failure Mode Recognition Clustering Algorithm...

Failure Mode Recognition Clustering Algorithm Based on Manifold Learning

Abstract:

Manifold learning is a new unsupervised learning method. Its main purpose is to find the inherent law of generated data sets. Be used for high dimensional nonlinear fault samples for learning, in order to identify embedded in high dimensional data space in the low dimensional manifold, can be effective data found the essential characteristics of fault identification. In many types of fault, sometimes often failure and normal operation of the equipment of some operation similar to misjudgment, such as oil pipeline transportation process, pipeline regulating pump, adjustable valve, pump switch, normal operation and pipeline leakage fault condition similar spectral characteristics, thus easy for pipeline leakage cause mistakes. This paper uses the manifold learning algorithm for fault pattern clustering recognition, and through experiments on the algorithm is evaluated.

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Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

2126-2130

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.2126

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Online since:

December 2012

Authors:

Zhi Gang Lou, Hong Zhao Liu

Keywords:

Laplacian Eigenmaps, Manifold Learning, Noise Reduction, Pattern Recognition, Pipeline Fault Diagnosis

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