ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018The following abstracts have been accepted for this event:

Online Deconvolution for Industrial Hyperspectral Imaging Systems
Authors: Yingying Song (CRAN)
Primary area of focus / application: Design and analysis of experiments
Keywords: Hyperspectral image, Online deconvolution, LMS, ZALMS
Submitted at 2May2018 02:32 by Yingying Song
Accepted
Pushbroom imaging systems make use of 2D sensors allowing to observe the scene linebyline at each time instant. The stream of spatialspectral arrays is stacked to form the hyperspectral image which is a 3D data cube. A hyperspectral image may suffer from spatial distortions resulting in a loss of spatial resolution. Assuming a constant acquisition velocity, any resolution improvement results in an increase of both blurring and noise level. Thus, the corresponding distortion can be modeled by linear invariant convolution.
We introduce a leastmeansquare (LMS) based framework accounting for the convolution kernel noncausality and including nonquadratic (zero attracting and piecewise constant) regularization terms. This results in the socalled sliding block regularized LMS (SBRLMS) which maintains a linear complexity compatible with realtime processing in industrial applications. The choice of these regularization terms is thus mainly motivated by the targeted application, namely, the inspection of objects put on a conveyor belt. At a given wavelength, the response of the conveyor after background removal is close to zero while that of the objects is supposed to be piecewise constant. A model for the algorithm mean and meansquare transient behavior is derived and the stability condition is studied.
Experiments were conducted to validate the transient behavior model with different regularization parameter values. This model allows to assess the influence of each regularization parameter. It appears that the zeroattracting property results in a faster convergence to zero than that of the algorithm without any regularization. The first order derivative regularizer is favoring the reconstruction of piecewise constant objects along the spatial dimension by decreasing the difference between two adjacent rows. However, both zeroattracting and the first order derivative properties introduce a bias on the amplitudes. More experiments showed that there exist optimal values for the different regularization parameters. Experimental results on both simulated and real hyperspectral images proved that the proposed SBRLMS outperforms standard approaches at low SNR scenarios which is the case corresponding to ultrafast scanning with industrial imaging systems.
Future works will focus on the automatic learning of hyperparameters. A joint online deconvolution and unmixing algorithm is also worth being studied. This is expected to yield a very low computational burden and accurate image restoration approach. 
Detection and Modelling of the Propagation Regimes in Fatigue Crack Propagation
Authors: Florine Greciet (Safran Aircraft Engines, Université de Lorraine, IECL, Inria BIGS), Romain Azaïs (Université de Lorraine, IECL, Inria BIGS), Anne GégoutPetit (Université de Lorraine, IECL, Inria BIGS)
Primary area of focus / application: Modelling
Keywords: Crack propagation, Modeling, Piecewise deterministic Markov Processes, EM algorithm, Trichotomic algorithm
Submitted at 3May2018 11:35 by Florine Greciet
Accepted
Crack propagation modeling goes through the study of the fatigue crack growth rate law according to the stress intensity factor. The following three regimes are empirically observable through the propagation law. In regime I, referred to as the crack initiation region, crack propagation is a discontinuous process which is extremely slow at very low values of stress intensity factor. In regime II, a powerlaw relationship between crack growth rate and stress intensity factor range is observed. Regime III corresponds to a quick and unstable crack growth leading to rupture when the stress intensity factor tends to a critical value.
To model the propagation law we propose to use Piecewise Deterministic Markov Processes (PDMPs) which are good candidates to model the three regimes mentioned before because it is a process governed by punctual random jumps governed by an ordinary differential equation between the jump times. It is important to note that times of transition between regimes are non observable on propagation curve because of these two following reasons: the crack propagation phenomenon is continuous and data are usually noisy.
Therefore, the trajectory is hidden, that means times of transition between regimes, propagation parameters in each regime and the number of propagation regimes are unknown.
The two methodologies that we propose aim at estimating all these quantities under the following assumptions:
 In each regime the propagation is described by a polynomial function;
 Regime changes preserve the propagation continuity;
 Trajectories are observed through a Gaussian additive noise.
The first methodology consists in assuming that the number of regimes is k and maximizing the likelihood with k regimes in two steps: the models parameters are estimated by an EM algorithm and we use an exhaustive search to find the times of transition. The number of regimes is determined a posteriori by a BIC criterion. The second approach is recursive and consists in estimating only one time of transition in the observed trajectory at each step. To overcome the invalidity of the model we maximize a penalized version of the likelihood by an EM algorithm (parameters estimation) through a trichotomic algorithm (estimation of transition times). When a time of transition is estimated, this procedure is repeated on the observed data after this time of transition. The procedure stops when the last estimated time of transition improves no more the BIC.
Results of simulations obtained for these two methods will be presented. 
Sequential Design of Experiments to Estimate a Probability of Failure in a MultiFidelity Stochastic Simulator
Authors: Séverine Demeyer (Laboratoire National de Métrologie et d'Essais), Rémi Stroh (Laboratoire National de Métrologie et d'Essais), Nicolas Fischer (Laboratoire National de Métrologie et d'Essais)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Metrology & measurement systems analysis
Keywords: Multifidelity, Sequential design, Bayesian analysis, Gaussian process, Probability of failure

Learning the Uncertainty Propagation Rate in Remaining Useful Lifetime Prediction
Authors: Yingjun Deng (Tianjin University), Alessandro Di Bucchianico (Eindhoven University of Technology), Mykola Pechenizkiy (Eindhoven University of Technology)
Primary area of focus / application: Modelling
Secondary area of focus / application: Reliability
Keywords: Remaining useful lifetime (RUL), Recurrent neural network, Uncertainty propagation rate, Inverse Gaussian random variable
Specifically, in this paper we use a recurrent neural network (RNN) as the RUL predictor. Later the RNN and the UPR are jointly trained with inspection data and failure records via alternative minimization. The proposed algorithms are applied to RUL prediction in an industrial use case within the Horizon 2020 Prophesy project. 
Industry 4.0: How to Support Complex Glass Construction by Statistics
Authors: Scheideler, Eva (OWL University of Applied Sciences), AhlemeyerStubbe, Andrea (Data Mining and More)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: Metamodeling, Curved glazing, Design of Experiment, Prediction modeling
Submitted at 7May2018 10:10 by Eva Scheideler
Accepted

Sensitivity Analysis and Constrained Multiobjective Optimization of Computer Experiments: A Case Study of a Centrifugal Compressor Impeller
Authors: Sonja Kuhnt (Dortmund University of Applied Sciences and Arts)
Primary area of focus / application: Other: Computer experiments
Keywords: Computer experiments, Kriging, Global optimization, Sensitivity analysis
Submitted at 8May2018 10:46 by Sonja Kuhnt
Accepted
Sensitivity analysis investigates how the input variables contribute to the variation of the outcome. A popular method is the use of Sobol indices which quantify the importance of individual input variables or groups of them. We review the Sobol sensitivity indices and the more recently developed total interaction indices (TII) and show how to display them in socalled FANOVA graphs. Besides increasing the knowledge about the unknown blackbox function, results from sensitivity analysis are useful within modeling and optimization.
When searching for a global optimum, the wellknown efficient global optimization (EGO) procedure considers prediction values as well as the model uncertainty. The compressor impeller application requires the optimization of multiple responses under additional constraints. We apply and compare possible extensions of the EGO algorithm.