Int J Adv Manuf Techno1 DO1 10.1007/s00170-0124399-2

Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

Ibrahim Masood . Adnan Hassan

Rwei~rd. I July 2U10 Acuepr~d: I6 July 2012 1: Syrmp;r-\'crlag Lunllon l.irniicd 2012

Abstract In multivariate quality control, the rutificial neu- ral networks (ANN)-based pattem recognition schemes gen- erally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charring. However, these schemes revealed disadvantages in term of reference bivariate pattern in identifying the joint effect and exeess false alarms in identfyhg stable process condition. In this study, feature-based ANN scheme was investigated for rec- ognizing bivariate correlated patterns. Featurebasal input representation was utilized into an ANN training and te* towards strengthening discrimination capability between bi- variate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate pattern, the proposed scheme promotes a smaller network size and better monitoring ca- pability as compared to the raw data-based ANN scheme.

Keywords Artificial neural networks . Bivariate emelated pattem . Process monitoring and diagnosis . Statistical features . Pattern recognition

L Masood (B) Faculty of Mechanical and Maouficturing Engineering, Universiti Tun Hussein Onn Mdaysia, 86400 Pait Rajq Baru Pahat, Johor, Malaysia e-mail: ibrahim@uthmedumy URL. hnp://im.uthin.edu.my

A. Hassan Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310UTM Sbd& Johor, Malaysia e-mail: adnan@h.utm.my WRC. http:l/w~\w.utm.rny

Abbreviations ANN Aaificial neural network ARL Average run length ART Adaptive resonance theory BPR Bivariate pattern recognition CCPR Control chart pattern recognition CUSUM Cumulative sum EWMA Exponentially weighted moving average FAR False alarm rate i.i.d. Identically and independently distributed LEEWMA Last value of exponentially weighted moving

average LVQ Learning vector quantization MAT (Mean) x (autocomelation) 1MGUSUM Multivariate cumulative sum MEWMA Multivariate exponentially weighted moving

average MLP Multilayer perceptrons M S V (Mean) x (mean square value) MQC Multivariate quality control MRWA Multi-resolution wavelet analysis MSD (Mean) x (standard deviation) MSE Mean square e m MSPC Multivariate statistical proeess control YLA k c i p l e component anaiysis RA Recognition a c c m y percentage RBF Wal basis function SOM Kohonen self-organizing mapping SPC Statistical process control traingdx Gradient decent with momentum and adaptive

leaming rate

1 Introduction

It is well known that variation in manufacluring processes has become a major source of poor quality. W~ear and tear,

a springs Published online: 04 August 2012

Int I Adv Manuf Teehnal

For the future work, we plan to conduct a more extensive investigation towards heightening the performance of the

feature-based ANN for identifying smaller mean shifts. In-

vestigation also wili be extended for other causable pa t te rn

such as trend and cyclic.

Acknowledgments The authors would like to thank Universiti Tun Hussein OnnMalaysia, Universiti Tehologi Malaysia, and M b b t r y of Higher Education of a y s i a who are sponsoring this work The constructive suggations h m the Editor in Chief of UAMT and the anonymous reviewers have greatly aided us to jmprove the contmts as well as the presentation of this paper

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