Attribute control charts for the compound Weibull-exponential distribution under truncated life tests

This study develops an attribute np control chart for monitoring the number of failures under truncated life tests using a single-sampling inspection plan, assuming that product lifetimes follow the Compound Weibull-Exponential (CWE) distribution. The control chart parameters are determined for various quality levels and specified in-control average run length (ARL0) values through an iterative design procedure. To provide a more comprehensive assessment of chart performance, the proposed scheme is evaluated using the Average Run Length (ARL1), Standard Deviation of the Run Length (SDRL), Expected Average Run Length (EARL), and Expected Quadratic Loss (EQL). Numerical tables are presented for different combinations of the shape parameter, sample size, specified ARL0, and process shift values. In addition, a Monte Carlo simulation study is conducted to demonstrate the performance of the proposed control chart for monitoring defective items.

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Publication Details

Journal
Journal of Statistical Computation and Simulation
Published
2026-09-04
DOI
https://doi.org/10.1080/00949655.2026.2715757
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
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article

Attribute control charts for the compound Weibull-exponential distribution under truncated life tests

Nazan Danacıoğlu
Journal of Statistical Computation and Simulation
Advanced Statistical Process Monitoring
article

Attribute control charts for the compound Weibull-exponential distribution under truncated life tests

Nazan Danacıoğlu
article en

Abstract

This study develops an attribute np control chart for monitoring the number of failures under truncated life tests using a single-sampling inspection plan, assuming that product lifetimes follow the Compound Weibull-Exponential (CWE) distribution. The control chart parameters are determined for various quality levels and specified in-control average run length (ARL0) values through an iterative design procedure. To provide a more comprehensive assessment of chart performance, the proposed scheme is evaluated using the Average Run Length (ARL1), Standard Deviation of the Run Length (SDRL), Expected Average Run Length (EARL), and Expected Quadratic Loss (EQL). Numerical tables are presented for different combinations of the shape parameter, sample size, specified ARL0, and process shift values. In addition, a Monte Carlo simulation study is conducted to demonstrate the performance of the proposed control chart for monitoring defective items.

Journal of Statistical Computation and Simulation
Sinop University (TR)
Responsible consumption and production
Openalex Percentile: Top 8%
Advanced Statistical Process Monitoring
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