ENBIS: European Network for Business and Industrial Statistics
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ENBIS Spring Meeting 2017
28 – 30 May 2017; Monastery of Schlägl in Upper Austria Abstract submission: 11 November 2016 – 5 March 2017The following abstracts have been accepted for this event:

Reliability Assessment of Failure Free Stress Tests
Authors: Horst Lewitschnig (Infineon Technologies Austria AG), Nikolaus Haselgruber (CIS Consulting in Industrial Statistics)
Primary area of focus / application: Reliability
Secondary area of focus / application: Reliability
Keywords: Life data analysis, Reliability methods, Robust validation, Zero defect
Submitted at 20Feb2017 05:30 by Horst Lewitschnig
Accepted
When it comes to product qualification, such devices should not and typically do not fail; even if the stress duration is extended.
Qualification tests are set up as zero failures sampling plans. This makes it difficult to estimate parameters of any underlying lifetime distribution, because such methods require failures.
We review alternative methods, how reliability as a function of time can be estimated even without failures in the stress test:
• One way is to use the ClopperPearson estimator for the failure probability after the stress test. For lower stress times, this value can be linearly scaled down.
• The ClopperPearson estimator can be regarded from a Bayesian point of view as the likelihood function combined with the a priori distribution Beta(0,1). When the parameters of this a priori Beta distribution are changed, based on expert knowledge, then this could lead to a more accurate estimation of the failure portion at the end of the stress test.
• The cumulative hazard function can be modelled by a Gammaprocess.
•The WeiBayes approach assumes a Weibull distribution with a given shape parameter.
We present an alternative approach. We use expert judgement, how the failure behavior beyond the censoring limit would be. In this way, we’re looking for failure scenarios that could occur if we would extend the stress test further. By doing this, we can fit lifetime distributions to those scenarios and finally assess them for the best fitting distribution as well as for a certain confidence level.
Basically we add more information to the stress test, which gives a more accurate picture about the devices’ reliability. If the stress test is really extended and failures occur, our assumptions are replaced step by step with real data; then the classical parameter estimators can be employed.
The work has been performed in the project eRamp (Grant Agreement N°621270), cofunded by grants from Austria, Germany, Slovakia and the ENIAC Joint Undertaking. 
Multi Failure Mode Reliability in Adults
Authors: Chris McCollin (Nottingham Trent University), Shirley Coleman (ISRU Necastle upon Tyne)
Primary area of focus / application: Reliability
Secondary area of focus / application: Modelling
Keywords: Failure modes, Langevin equation, Mokken scaling, Lévy process

Modeling the Variance of the Durations of Maintenance Activities in Semiconductor Fabs
Authors: Diamanta BensonKarhi (The Open University of Israel)
Primary area of focus / application: Reliability
Keywords: Availability, Variability of availability, Preventive Maintenance (PM), Imperfect PM, Optimization
Submitted at 21Feb2017 15:07 by Diamanta BensonKarhi
Accepted

Modeling the Lifespans of LithiumIon Cells by Using DOptimal Designs
Authors: Ernst Stadlober (University of Technology Graz, Institute of Statistics), Gerhard Gößler (Virtual Vehicle Research Center Graz), Martin Cifrain (Virtual Vehicle Research Center Graz)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Reliability
Keywords: Lithiumion cells, Cellaging experiments, Reliability, Design of Experiments, Response surfaces
Submitted at 21Feb2017 18:00 by Ernst Stadlober
Accepted
As a proper indicator of cell aging we define as life time of the cells the time needed to reach 70% of their initial capacity. It is also necessary to establish a method for extrapolating this life time, because a significant number of cells do not reach endoflife within the experiment. Simple exponential models lead to stable extrapolations with respect to the characteristics of the experimental settings.
The experiment is based on an initial design developed at the beginning of the project. This design includes seven factors (e.g. temperature, stateofcharge or current rates). However, the direct implementation of this design is not possible due to physical restrictions. Hence, we follow a strategy for revision of the design which focuses on a balance between constraints concerning available resources and the increase of the Defficiency of the design. The response surface can be reasonably described at least by two multiple regression models, the simpler one with six variables and the more complicated one with ten variables. Both models are able to discriminate between harsh and moderate validation points and indicate a satisfying quality of prediction. For conducting further experiments one of the suggested models may be used as a starting point for design construction. 
Applied and Interpretable Multivariate Process Control of Wafer Acceptance Tests
Authors: Peter Scheibelhofer (AMS AG), Günter Hayderer (AMS AG)
Primary area of focus / application: Process
Secondary area of focus / application: Reliability
Keywords: Statistical Process Control, Multivariate statistics, Robust statistics, Implementation, Semiconductor manufacturing
Submitted at 22Feb2017 15:27 by Peter Scheibelhofer
Accepted
measurements in a single control chart. The MasonYoungTrac decomposition of robust T^2 values offers the possibility to interpret outofcontrol observations, determining the single or multiple variables responsible for an abnormality. This way, univariate as well as multivariate deviations from the reference can be detected in a simple and practically favorable way. In order for the T^2 statistics to be useful and applicable an appropriate software environment was implemented in R and TIBCO Spotfire. The implementation is able to connect advanced statistical modeling with an easytouse interface for process engineers. 
The Use of Statistical Prediction Methods for Process Control of Additive Manufacturing
Authors: Eva Scheideler (OWL University oAS, Lemgo), Andrea AhlemeyerStubbe (Data Mining and More)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Additive manufacturing, Process control, Predictive modeling, Meta model
Submitted at 23Feb2017 08:47 by Eva Scheideler
Accepted
We want to develop a third category that will be used for powder bed fusion and non powder bed fusion related processes. It is based on combining metamodel thinking and predictive modeling.
This talk illustrates challenges and opportunities of applying statistical prediction methods and selflearning aspects to predict potential discontinuities. As a result we want to enable realtime regulation of laser power or other controllable parameters to avoid discontinuities of the additive manufactured parts. The talk will present the project set up and the actual work in progress.