Variable Importance Assessment in Lifespan Models of Insulation Materials: A Comparative Study

International audienceThis paper presents and compares different methods for evaluating the relative importance of variables involved in insulation lifespan models. Parametric and non-parametric models are derived from accelerated aging tests on twisted pairs covered with an insulating varnish under different stress constraints (voltage, frequency and temperature). Parametric models establish a simple stress-lifespan relationship and the variable importance can be evaluated from the estimated parameters. As an alternative approach, non-parametric models explain the stress-lifespan relationship by means of regression trees or random forests (RF) for instance. Regression trees naturally provide a hierarchy between the variables. However, they suffer from a high dependency with respect to the training set. We show that RF provide a more robust model while allowing a quantitative variable importance assessment. Comparisons of the different models are performed on different training and test sets obtained through experiments

Regression methods for improved lifespan modeling of low voltage machine insulation

International audienceThis paper deals with the modeling of insulation material lifespan in a partial discharge regime under certain accelerated electrical stresses (voltage, frequency and temperature). An original model, relating the logarithm of the insulation lifespan, the logarithm of the electrical stress and an exponential form of the temperature, is considered. An estimation of the model parameters is performed using three methods: the design of experiments (DoE) method, the response surface method (RSM) and the multiple linear regression (MLR) method. The estimation is obtained on learning sets determined according to each method specification. The performance, in terms of estimation, of each of the three methods is evaluated on a test set composed of additional experiments. For economic reasons and fl exibility, the learning and test sets are composed of experiments carried out on twisted pairs of wires covered by an insulator varnish. The ability of the DoE and the RSM methods to organize and to limit the number of experiments is confirmed. The MLR method, however, shows more flexibility with regard to the studied configurations. Thus, it offers an efficient solution when organization is not required or not possible. Moreover, the fl exibility of MLR allows specifi c ranges for the factors to be explored. A local analysis of the estimation performance shows that very short and long lifespans cannot be simultaneouslyrepresented by the same model

Lifespan modelling of low voltage machine insulation using multilinear regression

(Conférencier invité)International audienc

Hybrid parametric/non-parametric models for lifespan modeling of insulation materials

International audienceThis paper considers the problem of insulation material lifespan modelling. This problem is crucial for aircraft reliability since about 40% of electrical machine failures stem from insulation. According to the material physical properties and to the sightings reported in the literature, the proposed model should relate the logarithm of the insulation lifespan to the logarithm of the electrical stress factors (voltage and frequency) and to the temperature. The possible interactions between these three predominant aging factors must be also considered. Note that, due to a constraint of low experimental cost, the number and configuration of experiments were optimized through a design method. Parametric modelling through multilinear regression requires the estimation of a potentially high number of parameters in view of the reduced data set. The method proposed in this paper thus combines parametric and non-parametric approaches. First, a decision tree automatically classifies the experiments into ranges corresponding to relevant operating modes. Indeed, this method recursively partition the training set by selecting, at each node, the best separating variable and its best splitting value according to prediction performance. However, the predictive model produced by this tree is piecewise constant since a constant value is associated to each leaf of the tree. In this paper we combine this approach with parametric modelling, by associating a multilinear regression model to each region identified by the tree. This approach brings a better understanding of the aging phenomena through the hierarchical organization of the factors and also provides simple, specific and thus effective multilinear models in each lifespan range. The method performance is analysed through real data: training and test sets correspond to experiments on twisted pairs covered by an insulating varnish. The proposed method shows improved performance with respect to multilinear regression on one hand and to regression trees on the second hand

Parametric and non-parametric models for lifespan modeling of insulation systems in electrical machines

International audienceThis paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors (voltage, frequency and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization methods in order to minimize the experimental cost while ensuring the best model accuracy. In addition to classical parametric models, the life-stress relationship is expressed through original non-parametric and hybrid models that have never been investigated in insulation aging studies before. These two models present the original contribution of this paper. For each material, models are computed from organized sets of experiments and applied on a randomly configured test set for validity checking. The different models are evaluated and compared in order to define their optimal use

Statistical methods for the diagnosis of small-size training set models : application to the lifespan modelling of insulation materials

International audienceThis paper presents and compares statistical methods for evaluating the performance of parametric model estimation for insulation lifespan in the case of small size training sets. Parametric models are derived from accelerated aging tests on twisted pairs covered with an insulating varnish under different stress constraints (voltage, frequency and temperature). The estimation of the parametric model coefficients requires some hypothesis on the lifespan statistical distribution. However, since the number of measurements for each configuration is constrained by the experimental cost, the results given by classical goodness-to-fit tests and graphical tools may be questionable. This paper thus proposes to use the bootstrap technique for a more thorough statistical analysis. Indeed, bootstrap has been specifically designed to infer the statistical properties of an estimator when only few observations are available. In our case of study, the bootstrap technique confirms the results obtained using graphical tools and goodness-to-fit tests and thus the adequacy of the underlying statistical hypothesis required for model parameter estimation

Evaluating the feasibility, acceptability and pre testing the impact of a self-management and tele monitoring program for chronic obstructive pulmonary disease patients in Lebanon

International audienc

Parametric lifespan models for OLEDs using Design of Experiments (DoE)

International audienceModeling the lifespan of OLED (Organic Light-Emitting Diode) is a complex task: different factors may impact the lifespan, with possible interactions between them. However, the literature on this subject is still scant. This work proposes new parametric models for the OLED lifespan. For cost and accuracy reasons, the Design of Experiment (DoE) methodology is used for the estimation of model parameters. Different models are computed from thermal and electrical experimental aging tests. These innovative models involve, simultaneously, the current density, the temperature and their interactions, which are rarely taken into account in aging studies. The analysis of the model parameters highlights the prevalence of temperature compared to current density on the OLED luminance performance

Turbocharger Thermal Transfer Model Initialization: Quasi-Adiabatic Map Calculation

International audienc