A Variable Control Chart under the Truncated Life Test for a Weibull Distribution

Khan, Nasrullah; Aslam, Muhammad; Khan, Muhammad Zahir

doi:10.3390/technologies6020055

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…Ho and Quinino [2] proposed an attribute control chart for monitoring process variability. Khan et al [3] introduced the variable control chart under the truncated life test for Weibull distribution. Further details about attribute and variable control charts can be found in Epprecht et al [4], Chiu and Kuo [5], De Araujo Rodrigues et al [6], Joekes and Barbosa [7], Arif et al [8], and Rao et al [9] to mention but a few.…”

Section: Introductionmentioning

confidence: 99%

Attribute Control Chart for Rayleigh Distribution Using Repetitive Sampling under Truncated Life Test

Adeoti

Rao

2022

Journal of Probability and Statistics

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A control chart is an important tool in statistical process monitoring that is useful to monitor and improve production process quality. In this article, an attribute control chart using repetitive sampling under a truncated life test is proposed for monitoring the mean life of the product where the lifetime follows the Rayleigh distribution. The repetitive sampling parameters and the control limit coefficients of the chart are determined so that the in-control average run length (ARL) is very close to the target ARL. Tables of ARL values for various shift sizes in the scale parameter were presented, and the performance of the proposed chart is compared with the existing attribute control charts using the out-of-control ARL. The proposed control chart is shown to outperform the existing control charts in terms of ARL. An illustrative example is given to demonstrate the application of the proposed chart.

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Section: Introductionmentioning

confidence: 99%

Attribute Control Chart for Rayleigh Distribution Using Repetitive Sampling under Truncated Life Test

Adeoti

Rao

2022

Journal of Probability and Statistics

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“…The HEWMA chart has the ability to detect a small shift in the process and outperforms EWMA and mixed charts. Khan, Aslam, Kim, and Jun (2017) proposed a mixed control chart for a life test by assuming that the quality characteristics follow the Weibull distribution. However, those studies are only for a univariate case which is decomposed into variables and attributes and simultaneously monitored using a single control chart.…”

Section: Introductionmentioning

confidence: 99%

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Ahsan

Mashuri

Kuswanto

et al. 2018

Production & Manufacturing Research

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Two types of control charts exist based on different quality characteristics: variable and attribute. These characteristics are commonly monitored using separate procedures. Only a few studies focused on the utilization of control charts to monitor a process with mixed characteristics. This study develops a new concept of the T 2 control chart based on a Principal Component Analysis (PCA) Mix, that is a PCA method that can jointly handle continuous and categorical data. The Kernel Density Estimation (KDE) method is used to estimate the control limit. Through simulation studies, the performance of the proposed chart is evaluated using the Average Run Length (ARL). T 2 control limits obtained from KDE produce a stable ARL 0 at~370 for α ¼ 0:00273: For the shifted process, the proposed chart demonstrates excellent performance for an appropriate number of principal components used. Applications of the simulated process and real cases show that the proposed chart is sensitive to monitoring the shifted process.

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“…Here, Table 5) VE D = 193.3 Joules (from Table 4) p = 1.5 [25] T c = 8240 s = 2.29 h (from Table 5) H = 24 h/day s = 1 (sample size as one for expensive system and long-life MRI product [2]) L = 10 years [1] W = 50 weeks/year D = 6 days/week From Equations (10), (11), (12), and (13), test duration can be calculated as follows. Accelerated system reliability growth test duration is reduced from 537 days to 55 days, which is a remarkable achievement for a MRI system with one sample size.…”

Section: Calculating Acceleration Factor and Test Durationmentioning

confidence: 99%

“…These techniques are reliability growth test [4][5][6][7], reliability demonstration test [2], Crow Army Material Systems Analysis Activity (AMSAA) test [8][9][10][11], life test [12,13], accelerated life test [14], and burn in test [15] etc. One of the challenges to perform the MRI system reliability test is sample size due to very high sample cost.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Reliability Growth Test for Magnetic Resonance Imaging System Using Time-of-Flight Three-Dimensional Pulse Sequence

et al. 2019

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A magnetic resonance imaging (MRI) system is a complex, high cost, and long-life product. It is a widely known fact that performing a system reliability test of a MRI system during the development phase is a challenging task. The major challenges include sample size, high test cost, and long test duration. This paper introduces a novel approach to perform a MRI system reliability test in a reasonably acceptable time with one sample size. Our approach is based on an accelerated reliability growth test, which consists of test cycle made of a very high-energy time-of-flight three-dimensional (TOF3D) pulse sequence representing an actual hospital usage scenario. First, we construct a nominal day usage scenario based on actual data collected from an MRI system used inside the hospital. Then, we calculate the life-time stress based on a usage scenario. Finally, we develop an accelerated reliability growth test cycle based on a TOF3D pulse sequence that exerts highest vibration energy on the gradient coil and MRI system. We use a vibration energy model to map the life-time stress and reduce the test duration from 537 to 55 days. We use a Crow AMSAA plot to demonstrate that system design reaches its useful life after crossing the infant mortality phase.

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A Variable Control Chart under the Truncated Life Test for a Weibull Distribution

Cited by 9 publications

References 20 publications

Attribute Control Chart for Rayleigh Distribution Using Repetitive Sampling under Truncated Life Test

Attribute Control Chart for Rayleigh Distribution Using Repetitive Sampling under Truncated Life Test

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Accelerated Reliability Growth Test for Magnetic Resonance Imaging System Using Time-of-Flight Three-Dimensional Pulse Sequence

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