Paper Info
Title
Two Inverse Normalizing Transformation methods for the process capability analysis of non-normal process data.
Abstract
Display Omitted An Inverse Normalizing Transformation (INT) method is presented for PCIs of non-normal processes.A Simplified INT method using cubic spline interpolation is proposed to simplify its calculation.Simulation results show the INT method and Simplified INT method are superior to the existed ones.An example is given to show the advantage of these methods in terms of p value and non-conforming rate. For process capability analysis of non-normal processes, the non-normal data are often converted into normal data using transformation techniques, then use the conventional normal method to estimate the process capability indices (PCIs), and they are heavily affected by the transformation accuracy of the transformation methods. To enhance the transformation accuracy and improve the PCIs estimation, an Inverse Normalizing Transformation (INT) method is introduced to estimate PCIs for non-normal processes, and a Simplified INT method using cubic spline interpolation is further proposed to simplify its calculation. The performance of the proposed methods is assessed by a simulation study under Gamma, Lognormal and Weibull distributions, and simulation results show that the INT method and Simplified INT method perform better than the existed ones on the whole. Finally, a real case study is presented to show the application of the proposed methods.
Year
DOI
Venue
2016
10.1016/j.cie.2016.10.014
Computers & Industrial Engineering
Keywords
Field
DocType
Process capability indices,Non-normal distribution,Inverse Normalizing Transformation,Cubic spline interpolation,Box-Cox transformation,Root transformation
Process capability,Inverse,Mathematical optimization,Spline interpolation,p-value,Power transform,Algorithm,Weibull distribution,Log-normal distribution,Calculus,Mathematics
Journal
Volume
Issue
ISSN
102
C
0360-8352
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Hao Wang100.34
Jun Yang2447.91
Songhua Hao362.15