NDLI: Hybrid Abnormal Patterns Recognition of Control Chart Using Support Vector Machining

Content Provider	IEEE Xplore Digital Library
Author	Xiaoh Wang
Copyright Year	2008
Description	Author affiliation: Lab. of Numerical Control ofJiangxi Province, Jiujiang Univ., Jiujiang, China (Xiaoh Wang)
Abstract	A novel control chart pattern recognition system using support vector machine(SVM) is presented. Pattern recognition techniques have been wildly applied to identify abnormal patterns in control charts. Abnormal patterns exhibited by such charts can be associated with certain assignable causes affecting the process. Most of the existing recognition method are capable of recognizing a single abnormal pattern, however, a practical situation is concurrent patterns where two abnormal patterns may exist together. The presented method can enhance recognition capability and accuracy, and avoid the disadvantages, such us over-fitting, weak normalization capability, etc., of artificial neural network(ANN) method. Furthermore, it can recognize these hybrid abnormal patterns existing in control chart by combining voting and binary tree methods. Simulation experimental results are given to demonstrate that, compared with ANN recognition methods, the method proposed is superior in classifying shift, trend and cyclic patterns, and realized the recognition for hybrid abnormal pattern in control charts.
Starting Page	238
Ending Page	241
File Size	207278
Page Count	4
File Format	PDF
ISBN	9780769535081
DOI	10.1109/CIS.2008.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	control chart Machining Artificial neural networks Pattern recognition Security Support vector machines hybrid abnormal patterns support vector machine Support vector machine classification Control charts Binary trees Computational intelligence Classification tree analysis pattern recognition
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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