An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

Computational Intelligence and Neuroscience
Mahmoud Barghash

Abstract

Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

References

Apr 21, 2010·ISA Transactions·Ata Ebrahimzadeh, Vahid Ranaee
Jan 29, 2013·ISA Transactions·Patricia Henríquez RodríguezCarlos M Travieso

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Citations

Oct 12, 2016·Computational Intelligence and Neuroscience·Lin-Peng Jin, Jun Dong
May 27, 2017·Computational Intelligence and Neuroscience·Adnan O M AbuassbaHazrat Ali

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Software Mentioned

ARL
MathWorks Matlab
Ensemble

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