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Methods for interpreting the out‐of‐control signal of multivariate control charts: A comparison study
Author(s) -
Bersimis Sotiris,
Sgora Aggeliki,
Psarakis Stelios
Publication year - 2017
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2191
Subject(s) - artificial neural network , control chart , computer science , identification (biology) , control (management) , multivariate statistics , signal (programming language) , variable (mathematics) , artificial intelligence , machine learning , process (computing) , set (abstract data type) , control variable , data mining , mathematics , mathematical analysis , botany , biology , programming language , operating system
Multivariate control charts have proved to be a useful tool for identifying an out‐of‐control process. However, one of the main drawbacks of these charts is that they do not indicate which measured variables have been shifted. To overcome this issue, several alternative approaches that aim to diagnose faults the responsible variable(s) for the out‐of‐control signal and help identify aberrant variables may be found in the literature. This paper reviews several techniques that are used to diagnose the responsible variable(s) for the out‐of‐control signal and attempt to make a comparative study among them. In particular, we evaluate the performance of each method under different simulation scenarios in terms of successful identification of the out‐of‐control variables. Special attention has also been given to the computational approaches and especially in the ability of artificial neural networks to identify out‐of‐control signals. The obtained results show that: there is no particular method that can be considered as panacea; the artificial neural networks' performance depends heavily on the training data set.