Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks

Seyed Taghi Akhavan Niaki · Babak Abbasi

128 citations0/0 files storedQuality and Reliability Engineering International

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Advanced Statistical Process Monitoring Statistical Methods and Applications Fault Detection and Control Systems

Abstract

Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling's T2. Although T2 is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart-type multivariate control charts based on Hotelling's T2 are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright © 2005 John Wiley & Sons, Ltd.

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References

1992Introduction to Statistical Quality Control 1990Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements 1992A Multivariate Exponentially Weighted Moving Average Control Chart 2001Introduction to Statistical Quality Control 2000Statistical Methods for Quality Improvement 1988Multivariate Generalizations of Cumulative Sum Quality-Control Schemes 1996Multivariate SPC Methods for Process and Product Monitoring 1947Multivariate Quality Control

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2007A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes 2013Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines 2008A neural network applied to estimate process capability of non-normal processes 2011Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques 2008Designing a multivariate–multistage quality control system using artificial neural networks

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