ENBIS-7 in Dortmund

24 – 26 September 2007

My abstracts

 

The following abstracts have been accepted for this event:

  • Controlling Correlated Processes with Binomial Marginals

    Authors: Christian H. Weiß
    Primary area of focus / application:
    Submitted at 22-Jun-2007 15:27 by
    Accepted
    Few approaches towards the control of autocorrelated attribute data have been proposed in literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and wellinterpretable process model may be adequate. Based on known and newly derived statistical properties of this model, we will develop possible approaches to control such a process. A case study demonstrates the applicability of the binomial AR(1) model to SPC problems and allows to investigate the performance of the control charts suggested.
  • A Bayesian EWMA Method to Detect Jumps at the Start-up Phase of a Process

    Authors: Panagiotis Tsiamyrtzis and Douglas M. Hawkins
    Primary area of focus / application:
    Submitted at 22-Jun-2007 16:18 by
    Accepted
    The start-up phase data of a process are the spine of traditional SPC charting and testing methods and are usually assumed to be iid observations from the In Control distribution. In this work a new method is proposed to model Normally distributed start up phase data where we allow for serial dependence and bidirectional level shifts of the underlying parameter of interest. The theoretic development is based on a Bayesian sequentially updated EWMA model with Normal mixture errors. The new approach makes use of available prior information and provides a framework for drawing decisions and making prediction on line, even with a single observation.
  • INTEGRATING DATA AND MODEL UNCERTAINTIES IN PAINT FORMULATIONS

    Authors: Marco S. Reis, Pedro M. Saraiva and Fernando P. Bernardo (University of Coimbra, Coimbra, Portugal)
    Primary area of focus / application:
    Submitted at 22-Jun-2007 16:19 by
    Accepted
    Formulations frequently play a key role in rather different industrial applications (adhesives, additives, food, rubber, cosmetics, fertilizers and pesticides, photography, medicines, lubricants, perfumes, plastics, etc.). In spite of their relevance, the usual procedure to address such problems is still based upon extensive trial-and-error processes, usually quite inefficient and with rather limited success rates. Alternatively, in certain fields people have also developed deterministic optimization frameworks that take into account several quality-related product performance criteria, adequately constrained by relationships involving compositions or limits to which some components must comply. Such frameworks however neglect any sources of uncertainty and variability that may be present.
    Furthermore, both of the above approaches typically overlook potentially useful information contained in available databases, where data from previous trials is stored, that can (and in fact, should) be used to improve formulation solutions, namely through the estimation of statistical models relating key quality figures to composition variables.
    It is also desirable for a final consumer to get involved in the specification of a value hierarchical structure, so that the conceived product meets its desired specifications and unique preference structures.
    In this communication, we present a framework to develop and implement a robust approach for addressing and solving formulation problems, which:
    •Builds performance/composition relationships from past historical data;
    •Explicitly models and takes into account sources of variability and uncertainty characteristics;
    •Allows for the proper identification of specific customized optimal formulations for a given customer or specific product usage.
    This framework, although generic and easily applicable to other products, was tested within the scope of the paint industry, in order to support the proper identification of optimal waterborne paint formulations.
  • The effect of liberalization in the Italian gasoline sector: higher chance of collusion or incomplete liberalization?

    Authors: Alessando Fassò, Gianmaria Martini, Michele Pezzoni
    Primary area of focus / application:
    Submitted at 22-Jun-2007 16:30 by
    Accepted
    This paper aims to investigate, using a statistical approach, the impact of liberalization of the gasoline retail prices in Italy. The industry nowadays is characterized by an oligopoly made of vertically integrated companies holding a share of 98% of the distribution activities. Moreover gasoline can be classified as a good with a strong anelastic demand (at least in the short-run). These conditions are clearly favorable for an agreement between refiners. On the basis of a data set on the individual recommended gasoline daily prices from 1990 to 2005, the paper investigates two main issues. The first is the impact of some macroeconomic variables on the level of gasoline prices. Countless factors are involved in the generation of prices, first of all the crude oil price and the differences in euro/dollar exchange rate. Other factors like inflation, consumptions, production costs and taxation matter in fixing the price level. Moreover, strategic effects may also be important: the refiners may strategically react asymmetrically to oil price shocks, with immediate upward adjustments and delayed downward adjustments. The second one aims is the assessment of two nonexclusive hypotheses: the un-expected increase – after liberalization – in the observed retail price level, is due either to an increase in the degree of collusion among refiners, and/or to some restrictions to effective competition among retailers (e.g. limits in the opening time, and in the possibility to sell non oil goods, etc.). If the second hypothesis is confirmed, it will provide some evidence that the liberalization process in this sector is incomplete in Italy.
  • A note on smart alarming methods

    Authors: Laurent Bordes, Simplice Dossou-Gbété, Jean-Paul Valois (Laboratoire de Mathématiques Appliquées, France)
    Primary area of focus / application:
    Submitted at 22-Jun-2007 16:33 by
    Accepted
    Methods of Smart Alarming aim at timely novelty or anomaly detection in Data Streams. A review is proposed to highlight the key points of using them. In case of univariate data, the more suitable method is not the same of stationary variable or non-stationary variable. Multivariate data set are often dealt with unsupervised learning based methods, using either factor analysis (mostly PCA) or clustering algorithms. Each of these methods must be applied in a specific situation: the possible anomalies can be prior perfectly known or not, learning data set can be large sized or not, and so on. Some examples are outlined. Discussion underlines the importance to have a prior knowledge of variable behaviour, and to consider the global flow chart, including eventually a data preprocessing.
  • Prediction of Spiralling in BTA Deep-hole-drilling

    Authors: Amor Messaoud, Nils Raabe, Oliver Webber, Dirk Enk, Claus Weihs (University of Dortmund, Dortmund, Germany)
    Primary area of focus / application:
    Submitted at 24-Jun-2007 08:55 by
    Accepted
    Deep-hole drilling methods are used for producing holes with high length-to-diameter ratio, good surface finish and straightness.
    The process is subject to the occurrenec of a dynamic disturbances called spiralling. It leads to multi lobe-shaped deviation of
    the cross section of the hole from absolute roundness which constitutes a significant impairment of the workpiece. A common
    explanation for the occurrence of spiralling is the coincidence of time varying bending eigenfrequencies of the tool with multiples
    of the spindle rotation frequency. In practice, it is necessary that a process monitoring system is devised to predict the occurrence
    of spiralling during drilling. This allows the engineers to know when and how to adjust the process. In this work, the application
    and use of different monitoring strategies are discussed. These strategies are based on control charts in combination with a
    statistical and physical models describing the course of the eigenfrequencies.