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:

Evaluating the Environment of a Solar Inverter with Power Electronic Control Data
Authors: Bernhard Lanz (Fronius International GmbH)
Primary area of focus / application: Reliability
Keywords: Power electronics, Solar inverter, Reliability, Condition monitoring
The power module integrated sensors are used to control the power conversion. It is assumed that the sensor data from different systems can be used to get information about the environment of inverters. Statistical methods are applied to utilize the information provided by sensor data in order to explore the environmental conditions of an inverter, to trigger regeneration or to adjust condition monitoring functions. 
An Approach for Modelling System Reliability Based on Physics of Failure
Authors: Franz Moser (Statistische Anwendungen  Joanneum Research), Ulrike Kleb (Statistische Anwendungen  Joanneum Research), Birgit Kornberger (Statistische Anwendungen  Joanneum Research), Michaela Dvorzak (Statistische Anwendungen  Joanneum Research)
Primary area of focus / application: Reliability
Keywords: System reliability, Physics of failure, Dynamic stress, Survival signature
Submitted at 23Feb2017 17:54 by Franz Moser
Accepted
In semiconductor industry it is common practice to perform lifetime tests of single defects under constant stress conditions. The tests are done isolated for each relevant failure mechanism (such as hot carrier degradation, gate oxide breakdown or electromigration) to comply with technical standards. Furthermore the possibilities for implementing realistic test settings, like timevarying stress conditions, are limited. From the results of these tests, it is neither possible to derive the expected lifetime at system level nor dynamic stress can be taken into account.
The main task of our comprehensive approach is to improve reliability predictions at system level, considering physics of failure and dynamic stress conditions. It comprises the following elements:
(1) Definition of a system structure for a given reliability block diagram: First, the system components (vertices) and their interconnections (edges) have to be determined. As a device fails with the occurrence of just one type of failure, the failure mechanisms can be regarded as serial subsystem of the overall system. Thus, this step results in a serialparallel system, each component representing one failure mechanism, depicted as a graph.
(2) Calculating the Survival Signature: The Survival Signature is a concept that has been introduced for reliability assessment of systems. The procedure provides probabilities for the functioning of the system, given that a certain number of its components work. These probabilities characterize a given system structure and are not related to the survival functions of the components.
(3) Physics of Failure: This includes statistical modelling based on lifetime data from laboratory experiments (acceleration tests) under different stress conditions for every single failure mechanism. Aside from stress parameters, some design parameters such as transistor length are part of the proven failure distribution models.
(4) Dynamic stress: Only in rare cases electric variables in a microelectronic device behave constantly. To overcome this fact, a known approach was extended that enables to deal with dynamic voltages and currents. Based on stress profiles (waveforms), respectively clock cycles, that can be provided by circuit simulations, a time transformation factor, containing the total dynamic stress information (ESWF  Equivalent Stress Weighted Factor), is calculated. Applying this procedure, conventional (constant) stress models can still be used for lifetime prediction.
(5) The System Survival Probability at any time can be estimated with the failure distribution models, appropriately corrected by the ESWFs, and the probabilities arising from the survival signature. A complete system survival function will be generated by calculation of the above procedure for a sequence of defined time steps.
The approach got implemented in R shiny for demonstration purposes. An example of a simple inverter, serving as a test system, will be shown. 
Statistical Analysis of Vehicle/Track Interaction Data for Improved Predictive Maintenance
Authors: Gerald Trummer (Virtual Vehicle Research Center), Bernd Luber (Virtual Vehicle Research Center), Josef Fuchs (Virtual Vehicle Research Center), Luzia BurgerRinger (Institute of Statistics, Graz University of Technology)
Primary area of focus / application: Reliability
Keywords: Predictive maintenance, Multiple regression, Vehicle/track interaction, Railways
We focus on the standard deviation of the dynamic vertical and lateral forces on wheelsets which should be as low as possible from a maintenance point of view. These forces are linked to geometrical deviations and the track layout by multiple linear regression models obtained via stepwise procedures. Additional influences on the dynamic forces may then manifest themselves as deviations between measured and predicted values. The identification of such track sections is the focus of the described methodology.
Our results indicate that the possible causes for large deviations between prediction and measurement may be attributed to the lack of explanatory variables in the model, dynamic excitation of vehicles, or changes of the track superstructure properties. Some of these events occur infrequently in the data (e.g. bridges, switches) hence they were not identified as significant variables although they may occasionally lead to high dynamic vehicle forces. 
New Time Reduction Method for Burnin of Semiconductor Devices
Authors: Daniel Kurz (Department of Statistics, AlpenAdria University of Klagenfurt), Horst Lewitschnig (Infineon Technologies Austria AG), Jürgen Pilz (Department of Statistics, AlpenAdria University of Klagenfurt)
Primary area of focus / application: Reliability
Secondary area of focus / application: Quality
Keywords: Binomial distribution, Burnin time, Sampling plan, Weibull distribution, Zero defects
Submitted at 24Feb2017 11:20 by Daniel Kurz
Accepted
In this talk, we propose a new concept for assessing the BI time on the basis of the results from the BI study and a reference distribution for the lifetime of early failures. This involves to derive an upper bound for the total failure proportion from dependent binomial samples from different readout intervals. Furthermore, we provide suggestions on how to determine an appropriate reference curve for early failures. To avoid a too fast reduction of the BI time, we consider a delaying factor, which is based on the predictive probability of increasing the BI time.
Finally, the proposed concept is applied to determine sampling plans for reducing the BI time under different reduction strategies. Moreover, we extend the presented method to derive the BI times of follower products with different chip sizes. In this way, we are led to flexible BI times (of follower products), which essentially contributes to a reduction of the efforts of BI. 
Sparse Representation Based Approach for Change Detection
Authors: Ofer Levi (The Open University)
Primary area of focus / application: Modelling
Secondary area of focus / application: Mining
Keywords: Sparse representation, Change detection, Model selection, Least Squares, Fourier analysis, Wavelets
Submitted at 26Feb2017 10:17 by Ofer Levi
Accepted

Comparing Estimator Accuracy for the Weibull Distribution
Authors: Bryan Dodson (SKF Group Six Sigma), Rene Klerx (SKF Group Six Sigma), Marco Capozzi (SKF Group Six Sigma)
Primary area of focus / application: Reliability
Secondary area of focus / application: Six Sigma
Keywords: Weibull distribution, Reliability, Maximum Likelihood, Median rank regression
Submitted at 26Feb2017 17:29 by Marco Capozzi
Accepted