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A New Variable-Censoring Control Chart Using Lifetime Performance Index under Exponential and Weibull Distributions.
Aslam, Muhammad; Jeyadurga, P; Balamurali, S; Khan Sherwani, Rehan Ahmad; Albassam, Mohammed; Jun, Chi-Hyuck.
Affiliation
  • Aslam M; Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Jeyadurga P; Department of Mathematics, Kalasalingam University, Krishnankoil 626126, Tamilnadu, India.
  • Balamurali S; Department of Mathematics, Kalasalingam University, Krishnankoil 626126, Tamilnadu, India.
  • Khan Sherwani RA; College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan.
  • Albassam M; Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Jun CH; Department of Industrial and Management Engineering, POSTECH, Pohang 37673, Republic of Korea.
Comput Intell Neurosci ; 2021: 1350169, 2021.
Article in En | MEDLINE | ID: mdl-34987562
In reliability theory or life testing, exponential distribution and Weibull distribution are frequently considered to model the lifetime of the components or systems. In this paper, we design a control chart based on the lifetime performance index using Type II censoring for exponential and Weibull distributions. Average run length helps to measure the performance of the proposed control chart. The optimal values of the number of failure items and decision criteria used to decide whether the process is in-control or out-of-control based on the sample results are determined such that the in-control average run length is as close as to the specified average run length values. We simulate the data to illustrate the performance of the proposed control chart.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Statistical / Language Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Intell Neurosci Journal subject: INFORMATICA MEDICA / NEUROLOGIA Year: 2021 Document type: Article Affiliation country: Saudi Arabia Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Statistical / Language Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Intell Neurosci Journal subject: INFORMATICA MEDICA / NEUROLOGIA Year: 2021 Document type: Article Affiliation country: Saudi Arabia Country of publication: United States