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Change-Point-Based Statistical Process Controls

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Springer Handbook of Engineering Statistics

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Abstract

In the current era of computers, statistical monitoring of sequential observations is an important research area. In problems such as monitoring the quality of industrial products, health variables, climatological variables, etc., we are often interested in detecting a change in the process distribution in general, not just in mean or variance. We first briefly discuss a few commonly used SPC charts along with relevant references and then present a new chart for univariate continuous processes. Unlike most SPC charts in the literature, it neither assumes any “in-control” probability distribution nor requires any “in-control” Phase I data, and it aims to detect arbitrary distributional change. This chart uses a computationally efficient method to find the possible change-point. Moreover, the proposed chart uses a p-value-based data pruning approach to further increase the efficiency, and it combines the strengths of two different tests of hypotheses, which has a potentially broad application. Numerical simulations and two real-data analyses show that the chart can be used in various monitoring problems when the nature of distributional change is unknown.

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  1. Interdisciplinary Statistical Research Unit (ISRU), Indian Statistical Institute (ISI), Kolkata, West Bengal, India

    Partha Sarathi Mukherjee

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  1. Partha Sarathi Mukherjee
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Correspondence to Partha Sarathi Mukherjee .

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  1. Department of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ, USA

    Hoang Pham

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    Mukherjee, P.S. (2023). Change-Point-Based Statistical Process Controls. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-4471-7503-2_19

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