A weighted support vector machine method for control chart pattern recognition

    Authors

    P. Xanthopoulos;T. Razzaghi

    Comments

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    Abbreviated Journal Title

    Comput. Ind. Eng.

    Keywords

    Control chart; Pattern recognition; Weighted support vector machine; Classification; Imbalanced data; Quality control; STATISTICAL PROCESS-CONTROL; ARTIFICIAL NEURAL-NETWORK; IMAGE; CLASSIFICATION; DECISION TREE; SHIFTS; IDENTIFICATION; PERFORMANCE; EXTRACTION; IDENTIFY; FEATURES; Computer Science, Interdisciplinary Applications; Engineering, ; Industrial

    Abstract

    Manual inspection and evaluation of quality control data is a tedious task that requires the undistracted attention of specialized personnel. On the other hand, automated monitoring of a production process is necessary, not only for real time product quality assessment, but also for potential machinery malfunction diagnosis. For this reason, control chart pattern recognition (CCPR) methods have received a lot of attention over the last two decades. Current state-of-the-art control monitoring methodology includes K charts which are based on support vector machines (SVM). Although K charts have some profound benefits, their performance deteriorate when the learning examples for the normal class greatly outnumbers the ones for the abnormal class. Such problems are termed imbalanced and represent the vast majority of the real life control pattern classification problems. Original SVM demonstrate poor performance when applied directly to these problems. In this paper, we propose the use of weighted support vector machines (WSVM) for automated process monitoring and early fault diagnosis. We show the benefits of WSVM over traditional SVM, compare them under various fault scenarios. We evaluate the proposed algorithm in binary and multi-class environments for the most popular abnormal quality control patterns as well as a real application from wafer manufacturing industry. (C) 2014 Elsevier Ltd. All rights reserved.

    Journal Title

    Computers & Industrial Engineering

    Volume

    70

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    134

    Last Page

    149

    WOS Identifier

    WOS:000335542900013

    ISSN

    0360-8352

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