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Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

Abdoljalil Addeh1, Aminollah Khormali2, Noorbakhsh Amiri Golilarz3

  • 1Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.

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|May 9, 2018
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Summary
This summary is machine-generated.

This study introduces an optimized Radial Basis Function Neural Network (RBFNN) for recognizing eight control chart patterns (CCPs). The novel method enhances process monitoring by accurately identifying subtle variations in production data.

Keywords:
ARCCPRBFNNShape featuresStatistic features

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Area of Science:

  • Industrial Engineering
  • Artificial Intelligence
  • Statistical Process Control

Background:

  • Statistical Process Control (SPC) relies on control charts to monitor manufacturing processes.
  • Recognizing control chart patterns (CCPs) is crucial for identifying process deviations.
  • Distinguishing between similar patterns like Normal, Stratification, and Systematic poses a significant challenge.

Purpose of the Study:

  • To propose a novel method for enhanced control chart pattern recognition.
  • To improve the accuracy of identifying eight distinct CCPs, including challenging similar patterns.
  • To develop a robust system for continuous production process monitoring and control.

Main Methods:

  • A four-module approach: feature extraction (shape and statistical), feature selection (Association Rules), classification (Radial Basis Function Neural Network - RBFNN), and a novel learning algorithm (Bees Algorithm).
  • Utilized Association Rules (AR) for optimal feature selection from shape and statistical features.
  • Employed a Bees Algorithm-based learning module to optimize RBFNN performance.

Main Results:

  • The proposed RBFNN method demonstrated very good performance in recognizing eight CCPs.
  • Successfully distinguished between complex patterns such as Normal, Stratification, and Systematic.
  • Tested on a dataset of 1600 samples (200 per pattern) with high accuracy.

Conclusions:

  • The optimized RBFNN offers a powerful and accurate solution for CCP recognition.
  • This method significantly advances the capability of real-time production process monitoring.
  • The approach effectively addresses limitations of previous studies by including and distinguishing more complex patterns.