Statistical Reliability Engineering

2022 ◽  
Author(s):  
Hoang Pham
Author(s):  
Boris Gnedenko ◽  
Igor Pavlov ◽  
Igor Ushakov

1998 ◽  
Vol 47 (12) ◽  
pp. 1270-1275 ◽  
Author(s):  
Kazuo KITAGAWA ◽  
Takeshi SEMBA ◽  
Hiroyuki HAMADA

Author(s):  
C.K. Lakshminarayan ◽  
S. Pabbisetty ◽  
O. Adams ◽  
F. Pires ◽  
M. Thomas ◽  
...  

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3307
Author(s):  
Nirbhay Mathur ◽  
Vijanth Sagayan Asirvadam ◽  
Azrina Abd Aziz

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.


Economies ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 94
Author(s):  
Rui Silva ◽  
Margarida Simões ◽  
Ana Paula Monteiro ◽  
António Dias

This research aims to adapt the Leymann Inventory of Psychological Terror and its use on Portuguese Accounting Professionals. Leymann scale was applied in a final sample of 478 accountants aged between 28 and 68, of which 47.5% were men and 52.5% women. We used a quantitative methodology by applying a questionnaire survey whose results were, in the following research phase, analyzed using the statistical packages SPSS 26 and AMOS 27. We used SPSS 26 for the Descriptive Statistical Analysis and AMOS 27 to estimate the structural equation model that validated the scale. After the scale had been adapted and changed, it was validated in psychometric terms to be applied to and used in studies involving this type of professionals. The Accountants Leymann Inventory of Psychological Terror that resulted from adapting the original model was tested using structural equation modelling. Thus, the new scale produced significant results similar to those of the original scale, which means that it is valid and can be applied to other contexts. The validity and statistical reliability of the new scale made it possible to measure mobbing problems among accounting professionals reliably and robustly. The present research is an important contribution to the literature. Although it has been applied in several contexts, it is the first time it is being developed, adapted, and validated for accounting professionals who work in this area of management.


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