Monitoring process variability using decile mean standard deviation

2021 ◽  
Author(s):  
Moustafa Omar Ahmed Abu‐Shawiesh ◽  
Nadia Saeed
Keyword(s):  
Standard Deviation ◽  
Process Variability ◽  
Monitoring Process

Development of a Companion Set of Charts—Soft Sensor and Directional Moving Range—for Fault Monitoring, Detection and Diagnosis With Application to Gas Turbine Engines

2008 ◽  
Author(s):  
Dorin Scheianu ◽  
Phillip A. Farrington
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Research Literature ◽  
Soft Sensor ◽  
Fault Detection And Diagnosis ◽  
Critical Parameters ◽  
Multivariate Statistical ◽  
Process Variability ◽  
Monitoring Process ◽  
Detection And Diagnosis

Gas turbines monitoring for fault detection and diagnosis is long desired to be embedded within control systems. Yet the general approach is to have alarms and shut downs when critical parameters exceed certain limits, and fault diagnosis is initiated on the behalf of experienced professionals and testing apparatus during scheduled maintenance time. Statistical methods for monitoring univariate and multivariate processes have been developed and publicized in the research literature. A gas turbine can be treated as a complex multivariate process with parameters depending both on control variables imposed by operator and on independent ambient parameters. The authors propose a set of companion charts that can be implemented on line and allows continuous monitoring both for fault amplitude — represented by a newly introduced soft sensor — and for process variability in the direction of interest. The control limits are introduced using multivariate statistical theory. The set of charts was applied at Wood Group LIT in a test cell, for monitoring process variability and for diagnosis and characterization of engine faults during tests. A second application is used for early detection of faults at the current serviced fleet of turbines.

scholarly journals Nonparametric Double EWMA Control Chart for Process Monitoring

2016 ◽  
Vol 39 (2) ◽  
pp. 167 ◽  
Author(s):  
Muhammad Riaza ◽  
Saddam Akber Abbasib
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Average Run Length ◽  
Location Parameter ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Run Length ◽  
Ewma Control Chart ◽  
Monitoring Process ◽  
Nonparametric Techniques

<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations.</p>

Multiobjective design of an S control chart for monitoring process variability

2012 ◽  
Vol 2 (4) ◽  
pp. 408 ◽  
Author(s):  
Abdul Sattar Safaei ◽  
Reza Baradaran Kazemzadeh ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Control Chart ◽  
Process Variability ◽  
Monitoring Process ◽  
Multiobjective Design
Sign in / Sign up

Export Citation Format

Share Document