A New Distribution-Free Control Chart for Monitoring Process Median Based on the Statistic of the Sign Test

2021 ◽  
Vol 50 (1) ◽  
pp. 20210135
Author(s):  
Saber Ali ◽  
Zameer Abbas ◽  
Hafiz Zafar Nazir ◽  
Muhammad Riaz ◽  
Muhammad Abid
Keyword(s):  
Control Chart ◽  
Sign Test ◽  
Distribution Free ◽  
Monitoring Process ◽  
New Distribution

Diagnosing Problems of Distribution-Free Multivariate Control Chart

2014 ◽  
Vol 971-973 ◽  
pp. 1602-1606
Author(s):  
Wen Li Shi ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Historical Data ◽  
Important Application ◽  
Distribution Free ◽  
On Line ◽  
Multivariate Control Chart ◽  
Diagnostic Framework ◽  
Multivariate Control ◽  
Control Limits ◽  
New Distribution

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

Comparing Robustness of EWMA Dispersion Control Chart for Non-Normal Process

2014 ◽  
Vol 912-914 ◽  
pp. 1189-1192
Author(s):  
Hai Yu Wang
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Normal Process ◽  
Comparison Analysis ◽  
Run Length ◽  
Monitoring Process ◽  
Process Dispersion

This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.

scholarly journals Mixture Representations for the Joint Distribution of Lifetimes of two Coherent Systems with Shared Components

2013 ◽  
Vol 45 (4) ◽  
pp. 1011-1027 ◽  
Author(s):  
Jorge Navarro ◽  
Francisco J. Samaniego ◽  
N. Balakrishnan
Keyword(s):  
Representation Theorem ◽  
Joint Distribution ◽  
Survival Function ◽  
Coherent Systems ◽  
Comparative Performance ◽  
Component Failure ◽  
Distribution Free ◽  
Joint Survival Function ◽  
Signature Vector ◽  
New Distribution

The signature of a system is defined as the vector whose ith element is the probability that the system fails concurrently with the ith component failure. The signature vector is known to be a distribution-free measure and a representation of the system's survival function has been developed in terms of the system's signature. The present work is devoted to the study of the joint distribution of lifetimes of pairs of systems with shared components. Here, a new distribution-free measure, the ‘joint bivariate signature’, of a pair of systems with shared components is defined, and a new representation theorem for the joint survival function of the system lifetimes is established. The theorem is shown to facilitate the study of the dependence between systems and the comparative performance of two pairs of such systems.

scholarly journals PENERAPAN DIAGRAM KONTROL MEWMA DAN MEWMV PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Kualitas Pengolahan Air II PDAM Tirta Moedal Kota Semarang)

2018 ◽  
Vol 7 (1) ◽  
pp. 23-32
Author(s):  
Adestya Ayu Maharani ◽  
Mustafid Mustafid ◽  
Sudarno Sudarno
Keyword(s):  
Water Treatment ◽  
Control Chart ◽  
Moving Average ◽  
Process Capability ◽  
Human Consumption ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Monitoring Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Water is one of the most important elements for human life, water treatment is done for human consumption and must fulfill the health requirements with the levels of certain parameters. Quality of Water Treatment II is the second water purification installation owned by PDAM Tirta Moedal Semarang City with production capacity of 60 l/s. Variables used in the water treatment process are correlated with each other, so used multivariate control chart. The Multivariate Exponentially Weighted Moving Average control chart is used for monitoring process mean, and the Multivariate Exponentially Weighted Moving Variance control chart is used for monitoring process variability. The variables used are colour, turbidity, organic substance, manganese and the total dissolved solid. MEWMA control chart with λ = 0.5, showed that the process mean is controlled statistically. MEWMV control chart showed that variability is controlled statistically in λ = 0.4, ω = 0.2 and L = 3.3213. MEWMA and MEWMV control chart showed that the process is not capable because it obtained the value of process capability index less than 1. Keywords: Water, Multivariate Exponentially Weighted Moving Average, Multivariate Exponentially Weighted Moving Variance, process capability.

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