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Novel approaches for statistical process control chart pattern recognition.

机译:统计过程控制图模式识别的新方法。

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摘要

Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.
机译:快速准确地识别统计控制图模式(SPCCP)对于监督制造过程以实现更好的控制和制造高价值产品具有重要意义。 SPCCP可以显示八种模式:正常,分层,系统,上升趋势,下降趋势,上移,下移和周期性。除自然模式外,所有其他模式均表示受监管的制造过程无法正常执行,因此需要采取措施来纠正问题。这项研究为(SPCCP)识别提出了新的方法,神经网络和神经模糊系统。本文还研究了从统计分析中提取的特征用于简单模式,将小波分析中的并发模式用作输入向量的组成部分。基于模拟数据的结果表明,所提出的方法比常规方法具有更好的性能。我们的工作得出的结论是,提取出的特征可改善建议的识别器系统的性能。

著录项

  • 作者

    Elhomani, Abdellatif.;

  • 作者单位

    Southern Illinois University at Carbondale.;

  • 授予单位 Southern Illinois University at Carbondale.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 67 p.
  • 总页数 67
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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