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Development and Validation of an Adaptive Quantile Monitoring Framework for Alpha Power Weibull Process under Censoring.

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Title: Development and Validation of an Adaptive Quantile Monitoring Framework for Alpha Power Weibull Process under Censoring.
Authors: Atif, Muhammad1 (AUTHOR), Akhtar, Noureen1 (AUTHOR), Ramzan, Qasim1,2 (AUTHOR) qasimramzankbk@gmail.com, Abbas, Sami1 (AUTHOR), Nazir, Hafiz Zafar1 (AUTHOR), Alballa, Tmader3 (AUTHOR)
Source: Quality & Reliability Engineering International. Jun2026, Vol. 42 Issue 4, p1580-1593. 14p.
Subjects: Weibull distribution, Censoring (Statistics), Wind turbine efficiency, Supervisory control & data acquisition systems, Quality control charts
Abstract: Monitoring systems are critical tools across scientific and engineering domains for detecting shifts in lifetime or performance‐related phenomena, especially under censoring conditions. In renewable energy systems, statistical control charts for censored data play a pivotal role in tracking wind speed distributions to identify operational anomalies, particularly when Supervisory Control and Data Acquisition (SCADA) sensors fail, are removed for maintenance, or data is intermittently lost. This study introduces a novel Adaptive framework for the Alpha Power Weibull distribution's Quantile under progressive type‐II Censored data. The proposed scheme leverages the Quantile Function of the Alpha Power Weibull Distribution (APWD) and an adaptive exponentially weighted moving average (AEWMA) chart, specifically tailored for wind turbine performance monitoring. Maximum likelihood estimators for APWD parameters under PT‐II censoring form the foundation of the novel scheme. A comprehensive simulation study evaluates in‐control and out‐of‐control performance using average run length, while application to real‐world wind turbine SCADA data confirms its ability to detect out‐of‐control signals. The framework is extensible to Weibull, generalized exponential, Rayleigh, and exponential distributions across various censoring schemes. [ABSTRACT FROM AUTHOR]
Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 193399626
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Development and Validation of an Adaptive Quantile Monitoring Framework for Alpha Power Weibull Process under Censoring.
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  Data: <searchLink fieldCode="JN" term="%22Quality+%26+Reliability+Engineering+International%22">Quality & Reliability Engineering International</searchLink>. Jun2026, Vol. 42 Issue 4, p1580-1593. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Weibull+distribution%22">Weibull distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Censoring+%28Statistics%29%22">Censoring (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+turbine+efficiency%22">Wind turbine efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Supervisory+control+%26+data+acquisition+systems%22">Supervisory control & data acquisition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Monitoring systems are critical tools across scientific and engineering domains for detecting shifts in lifetime or performance‐related phenomena, especially under censoring conditions. In renewable energy systems, statistical control charts for censored data play a pivotal role in tracking wind speed distributions to identify operational anomalies, particularly when Supervisory Control and Data Acquisition (SCADA) sensors fail, are removed for maintenance, or data is intermittently lost. This study introduces a novel Adaptive framework for the Alpha Power Weibull distribution's Quantile under progressive type‐II Censored data. The proposed scheme leverages the Quantile Function of the Alpha Power Weibull Distribution (APWD) and an adaptive exponentially weighted moving average (AEWMA) chart, specifically tailored for wind turbine performance monitoring. Maximum likelihood estimators for APWD parameters under PT‐II censoring form the foundation of the novel scheme. A comprehensive simulation study evaluates in‐control and out‐of‐control performance using average run length, while application to real‐world wind turbine SCADA data confirms its ability to detect out‐of‐control signals. The framework is extensible to Weibull, generalized exponential, Rayleigh, and exponential distributions across various censoring schemes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/qre.70153
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 1580
    Subjects:
      – SubjectFull: Weibull distribution
        Type: general
      – SubjectFull: Censoring (Statistics)
        Type: general
      – SubjectFull: Wind turbine efficiency
        Type: general
      – SubjectFull: Supervisory control & data acquisition systems
        Type: general
      – SubjectFull: Quality control charts
        Type: general
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      – TitleFull: Development and Validation of an Adaptive Quantile Monitoring Framework for Alpha Power Weibull Process under Censoring.
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            NameFull: Atif, Muhammad
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            NameFull: Akhtar, Noureen
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            NameFull: Ramzan, Qasim
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            NameFull: Nazir, Hafiz Zafar
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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