Title of Paper:Unsupervised Diagnostic and Monitoring of Defects Using Waveguide Imaging With Adaptive Sparse Representation

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Journal:IEEE Transactions on Industrial Informatics

Abstract:This paper proposes a new system for the unsupervised diagnostic and monitoring of defects in waveguide imaging. The proposed method is automatic and does not require manual selection of specific frequencies for defect diagnostics. The core of the method is a computational intelligent machine learning algorithm based on sparse non-negative matrix factorization. An internal functionality is built into the machine learning algorithm to adaptively learn and control the sparsity of the factorization, and to render better accuracy in detecting defects. This is achieved by using Bayesian approach.

All the Authors:Bin Gao,Wai Lok Woo,Guiyun Tian,Hong Zhang

Discipline:Engineering

Volume:12

Issue:1

Page Number:405-416

Translation or Not:no

Date of Publication:2016-02-02