Signal Detection for Process with Unknown Distribution

Abstract:

Control charts are effective tools for monitoring both manufacturing processes and service processes. Much service data comes from a process with variables having non-normal or unknown distributions. The commonly used Shewhart variable control charts which depend heavily on the normality assumption should not be applied here. Hence, an alternative is desired to handle these types of process data. In this paper, we propose a new Variance Chart based on a simple statistic to monitor process variance shifts. The sampling properties of the new monitoring statistic are explored. A numerical example of service times from a bank service system with a right skewed distribution is used to illustrate the proposed Variance Chart. A comparison with two existing charts is also performed. The Variance Chart showed better ability than those two charts in detecting shifts in the process variance.

• Periodical: Advanced Materials Research (Volumes 503-504)
• Pages: 1472-1475
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.503-504.1472
• Online since: April 2012
• Authors: Su Fen Yang, Barry C. Arnold
• Keywords: Average Run Length, Binomial Distribution, Process Variance, Skewed Distribution, Variance Chart

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