ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

My abstracts

 

The following abstracts have been accepted for this event:

  • Hierarchical Models for Reliability Verification

    Authors: Nikolaus Haselgruber (CIS Consulting in Industrial Statistics GmbH)
    Primary area of focus / application: Reliability
    Secondary area of focus / application: Modelling
    Keywords: Reliability, Hierarchical Bayesian models, Verification, Sampling plans
    Submitted at 6-Mar-2018 12:03 by Nikolaus Haselgruber
    Accepted (view paper)
    4-Sep-2018 09:00 Hierarchical Models for Reliability Verification
    In a development process of a complex technical product, such as a vehicle or a wind turbine, the verification of reliability targets is an important mission. It decides about release of systems and components. If the product can be considered as a repairable system composed of (subsystems built up from) non-repairable components, the verification process starts on component level and ends up with the release of the final product based on system testing. Depending on reliability and lifetime requirements, the verification process may be expensive in terms of resources and time. Thus, effective sampling plans can help to minimize the sample size and simultaneously control the risk of releasing unreliable products. This talk discusses several ways to design reliability verification plans. Starting from the classical binomial approach which is well-known from quality engineering, several alternatives are introduced, such as a Bayesian variant with Beta prior on the failure probability or an a priori known shape parameter for Weibull distributed lifetime. Applications of hierarchical Bayesian models to derive sampling plans for reliability verification for non-repairable as well as repairable objects are presented in addition. Advantages and potential problems of the different alternatives are discussed by looking real-world examples from the automotive industry, e.g. head lamps of passenger cars.
  • A New Age and State-Dependent Degradation Process with Possibly Negative Increments

    Authors: Massimiliano Giorgio (Università della Campania "Luigi Vanvitelli"), Gianpaolo Pulcini (Istituto Motori, National Research Council (CNR))
    Primary area of focus / application: Other: Statistical methods for degradation data
    Keywords: Degradation, Wiener process, Age and state dependent degradation increments, Maximum likelihood estimation
    Submitted at 7-Mar-2018 14:46 by massimiliano Giorgio
    Accepted
    5-Sep-2018 10:50 A New Age and State-Dependent Degradation Process with Possibly Negative Increments
    A new age and state-dependent degradation process is proposed that can be used in the case the degradation phenomenon under study is not necessarily monotonic increasing. In particular, the degradation increments of the proposed model are assumed to be possibly dependent on each other and negative. The model is obtained by generalizing the well-known and largely applied Wiener process and partially preserves the mathematically tractability of the Wiener process. In the paper, the main features and properties of the proposed model are first discussed and the maximum likelihood estimators of its parameters are derived. An applicative example is finally developed which shows the feasibility and the effectiveness of the proposed approach.
  • Testing for Lack of Fit in Blocked, Split-Plot, and Other Multi-Stratum Designs

    Authors: Peter Goos (KU Leuven), Steven Gilmour (King's College)
    Primary area of focus / application: Design and analysis of experiments
    Keywords: Replication, Residual Maximum Likelihood (REML), Split-Plot Design, Treatment model
    Submitted at 9-Mar-2018 21:02 by Peter Goos
    Accepted
    4-Sep-2018 15:20 Testing for Lack of Fit in Blocked, Split-Plot, and Other Multi-Stratum Designs
    Textbooks on response surface methodology emphasize the importance of lack-of-fit tests when fitting response surface models and stress that, to be able to test for lack of fit, designed experiments should have replication and allow for pure-error estimation. In this paper, we show how to obtain pure-error estimates and how to carry out a lack-of-fit test when the experiment is not completely randomized, but a blocked experiment, a split-plot experiment, or any other multi-stratum experiment. Our approach to calculating pure-error estimates is based on residual maximum likelihood (REML) estimation of the variance components in a full treatment model (sometimes also referred to as a cell means model). It generalizes the approach suggested by Vining et al. (2005) in the sense that it works for a broader set of designs and for replicates other than center-point replicates. Our lack-of-fit test also generalizes the test proposed by Khuri (1992) for data from blocked experiments because it exploits replicates other than center-point replicates and works for split-plot and other multi-stratum designs as well. We provide analytical expressions for the test statistic and the corresponding degrees of freedom and demonstrate how to perform the lack-of-fit test in the SAS procedure MIXED. We re-analyze several published data sets and discover a few instances in which the usual response surface model exhibits significant lack of fit.
  • Design of a Multivariate Control Chart Using a Support Vector Machine

    Authors: Paria Soleimani (Department of Industrial Engineering, South Tehran branch, Islamic Azad University, Tehran, Iran), Amir Azar (Department of Industrial Engineering, South Tehran branch, Islamic Azad University, Tehran, Iran)
    Primary area of focus / application: Process
    Keywords: Statistical process control, Multivariate control chart, Support vector machine, Kernel functions, Average run length
    Submitted at 12-Mar-2018 09:35 by Amir Azar
    Accepted (view paper)
    3-Sep-2018 15:50 Design of a Multivariate Control Chart Using a Support Vector Machine
    The current research has designed and developed a control chart based on a one-class support vector machine and has generalized it to the nonlinear state using kernel functions. The proposed method, taking into account the distribution of data, can create the complex structures as the control limits. In this research with the help of other decision functions, the possibility of discovering out of control variable is also possible that, this feature is not available in most traditional charts.

    In order to make a correct comparison, the average run length criterion in a controlled state by adjusting the control limits has been set to 200. Consequently, the control super-space is achieved. Regarding to the degree of kernel function used in the design of the chart, control limits can be converted from the control circle to the control ellipse and ultimately converted to the ellipses of one side (bean shape).

    Following are the comparisons between the designed chart and the traditional Hotelling T2chart indicating that, the proposed method has better performance in terms of the average run length. In addition, the discovery of the uncontrolled variable is also evaluated by the proposed method that in this context, the control chart based on support vector machine with polynomial kernel function has been superior.
  • Estimating Latent Variable by Generalized Kalman Recursions

    Authors: Sadeq Kadhim (Université de Lorraine), Joseph Ngatchou-Wandji (Université de Lorraine)
    Primary area of focus / application: Modelling
    Secondary area of focus / application: Quality
    Keywords: Generalized Kalman recursions, Generalized state space models, Multicategorical longitudinal data, Latent variables, Particle filters, EM algorithm
    Submitted at 16-Mar-2018 09:54 by Sadeq KADHIM
    Accepted
    We consider state space models, relate observed time series to unobserved time series by a system of two equations. The first equation is called the observation equation and the second equation is called the state equation. In this work, we consider generlaized state space models in which the associated functions can be non-linear and the noises of non-Gaussian models. We estimate the states of these models using generalized Kalman recursions, the particle filters and the EM algorithm. Our work is focused on estimating the latent trait in quality of life where the questionnaires are considered as observations and latent variables as states. Concrete examples of latent variables are: the patient health, the business confidence, the morale of customers, the level of anxiety of machine or robot users in factories. Our approach is both an alternative to existing literature work and its generalization. In our work, we are interested in the latent variables Xi (t) produced by an individual i, (i = 1, · · · , n), at time t, (t = 1, · · · , T ). Xi (t) may be the patient’s health, a latent trait, etc. Only Yi (t) is observed instead of Xi (t), the Yi (t) are individuals’ responses to the questionnaire. These results are illustrated by numerical simulations and an application to real data in the quality of life of patients surged for breast cancer.
  • Effect of Several Failure Imputation Methods in Estimating the Survival Function under Interval Censoring

    Authors: Mario C. Jaramillo-Elorza (Universidad Nacional de Colombia), Carlos M. Lopera-Gómez (Universidad Nacional de Colombia)
    Primary area of focus / application: Reliability
    Secondary area of focus / application: Modelling
    Keywords: Survival analysis, Interval censoring, Data imputation, Turnbull's method
    Submitted at 19-Mar-2018 15:53 by Carlos M. Lopera-Gómez
    Accepted (view paper)
    4-Sep-2018 14:30 Effect of Several Failure Imputation Methods in Estimating the Survival Function under Interval Censoring
    Most survival analyzes are based on exact failure times and right censored observations, using widely used statistical methods such as the Kaplan-Meier (KM) method. In medical studies the failure times (e.g. disease or relapse) are observed in the visits that pacients do to medical centers and this situation induces that the interval censoring arises. When interval censoring is present in data, it is necessary to use the Turnbull's method to estimate the survival function, however in practice the imputation of the failure time in this type of censoring is often done using the midpoint of the interval, the right end of the interval or a random point generated within the interval using the uniform distribution. This work through simulation studies the effect of the three types of imputation on the estimation of the survival curve compared to the Turnbull's method. Different simulation scenarios were analyzed based on sample size and time between visits. In all simulation scenarios, the functions estimated using data imputation differ significantly from the true survival function S(t).