Abstract
In statistical process control (SPC) there are two situations where monitoring multivariate is needed. One is that all of the variables monitored are product ones. The other is that the variables monitored are some product and process ones. In these cases, there are correlations among the variables. Therefore, application of multivariate control charts to such process control is useful.
In this chapter, the latter case of monitoring causality is addressed. It is known that T 2–Q control charts, which are modified from standard multivariate control charts utilizing Mahalanobis distance, are an effective SPC tool. However, in using multivariate control charts, diagnosis is not so easy. The objective in this chapter is to propose a diagnostic method for identifying an unusual causal relationship in a process causal model and then to examine its performance.
Our proposed method is to identify the nearest unusual model by utilizing the Mahalanobis distance between some supposed unusual models and the data indicating the out of control in Q charts.
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Acknowledgement
This work was supported by KAKENHI (25750120).
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Nishina, K., Kawamura, H., Okamoto, K., Takahashi, T. (2018). Monitoring and Diagnosis of Causal Relationships Among Variables. In: Knoth, S., Schmid, W. (eds) Frontiers in Statistical Quality Control 12. Frontiers in Statistical Quality Control. Springer, Cham. https://doi.org/10.1007/978-3-319-75295-2_10
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DOI: https://doi.org/10.1007/978-3-319-75295-2_10
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