Selection of Sampling Inspection Plans for Life Tests Based on Lognormal Distribution

2015 ◽  
Vol 44 (5) ◽  
pp. 20150206 ◽  
Author(s):  
R. Vijayaraghavan ◽  
S. Uma
Keyword(s):  
Lognormal Distribution ◽  
Sampling Inspection ◽  
Life Tests ◽  
Selection Of

Six Sigma metrics based on lognormal distribution for life tests

2019 ◽  
Vol 36 (9) ◽  
pp. 1477-1489 ◽  
Author(s):  
Ravichandran Joghee
Keyword(s):  
Six Sigma ◽  
Lognormal Distribution ◽  
Tail Probability ◽  
Tail Probabilities ◽  
Content Type ◽  
Underlying Distribution ◽  
Left Tail ◽  
Long Run ◽  
Life Tests ◽  
The Right

Purpose The purpose of this paper is to propose an approach for studying the Six Sigma metrics when the underlying distribution is lognormal. Design/methodology/approach The Six Sigma metrics are commonly available for normal processes that are run in the long run. However, there are situations in reliability studies where non-normal distributions are more appropriate for life tests. In this paper, Six Sigma metrics are obtained for lognormal distribution. Findings In this paper, unlike the normal process, for lognormal distribution, there are unequal tail probabilities. Hence, the sigma levels are not the same for left-tail and right-tail defects per million opportunities (DPMO). Also, in life tests, while left-tail probability is related to DPMO, the right tail is considered as extremely good PMO. This aspect is introduced and based on which the sigma levels are determined for different parameter settings and left- and right-tail probability combinations. Examples are given to illustrate the proposed approach. Originality/value Though Six Sigma metrics have been developed based on a normality assumption, there have been no studies for determining the Six Sigma metrics for non-normal processes, particularly for life test distributions in reliability studies. The Six Sigma metrics developed here for lognormal distribution is new to the practitioners, and this will motivate the researchers to do more work in this field of research.

Selection of Modified Lot Sensitive Sampling Inspection Plans for Isolated Lots With Small Acceptance Numbers

2013 ◽  
Vol 41 (5) ◽  
pp. 20120331
Author(s):  
R. Vijayaraghavan ◽  
A. Loganathan ◽  
D. Rajalakshmi
Keyword(s):  
Sampling Inspection ◽  
Selection Of

scholarly journals A Procedure for the Selection of Single Sampling Plans by Variables Based on Pareto Distribution

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Geetha Sathya Narayanan ◽  
Vijayaraghavan Rajarathinam
Keyword(s):  
Pareto Distribution ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Implicit Assumption ◽  
Sampling Plans ◽  
Nonnormal Distributions ◽  
Sampling Inspection ◽  
Continuous Scale ◽  
Selection Of ◽  
Product Control

Product control in statistical quality control is the methodology that deals with procedures for taking decisions about one or more lots of finished products manufactured by production processes. Sampling inspection by variables is one of the major classifications of product control and comprises procedures for deciding about the disposition of a lot of individual units based on sample measurements of units on a quality characteristic under study. These procedures are defined under the assumption that the quality characteristic is measurable on a continuous scale, and the functional form of the probability distribution must be known. Inspection procedures which have been developed under the implicit assumption that the quality characteristic is distributed as normal with the related properties are found in the literature of product control. The assumption of normality may not be realized often in practice, and it becomes indispensable to investigate the properties of sampling plans based on nonnormal distributions. In this paper, a single sampling plan by variables is formulated and evaluated when the quality characteristic is assumed to be distributed according to a Pareto distribution. A procedure is developed for determining the parameters of the plan for specified requirements to ensure protection to both the producer and consumer.

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