**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Publication# Vieillissement et fiabilité des parcs de poteaux bois des réseaux de lignes aériennes

Abstract

The reliability of new overhead electric and telecommunication lines depends principally on the quality of their support structures. These structures are generally made of wood, metal or concrete. The complexity of a natural substance such as wood requires a thorough analysis of the various factors that influence its overall quality. In the case of wood poles, such factors include initial forest growth pattern, the species of wood and its preservative treatment, ageing characteristics, and its various mechanical defects such as knots, cracks etc. The accumulation of knowledge on the effect of the various variables that contribute to the overall quality of a wood support structure permits an optimum use of such a resource. For example, less variability and higher strength of wood support structures permits optimum loading and spacing between structures, thus reducing the number needed in a specific length of an overhead line. If one assumes that in Western Europe 1 wood pole is employed for every 2 inhabitants, and that this proportion increases in less densely populated countries such as the US and Scandinavia, the economics of optimum use of wood as a resource soon become apparent. In less developed countries, the proportions and the economics vary depending on the natural resources such as wood that they employ. The goal of this research is to establish, thanks to non destructive evaluations, a general ageing probabilistic law of the wooden pole based on two distinguished laws: one on the new pole in studying the influence of a grading of the bad elements based on a normal law: "left-truncation of a normal distribution", point 1; and another one based on the in-field wooden pole in exploiting the different parameters such as: the age of the pole, its chemical treatment, its species, its knots etc. in order to define the pole's damage law, point 2. Statistical distribution law of the new wooden pole after grading by non destructive sorting (ultrasounds) of the high mechanical performances supports: This new distribution law is a Gaussian law or evolves to a Log or Weibull's law with 3 parameters according to the inspected species. This grading allows a revalorization of the properties of the new poles and of the design values while guaranteeing an index of reliability required by the design standards, or in improving directly this nominal reliability (economic gain and reliability gain). Statistical distribution law of an aged in-field population (20-50 years old) approached by a bi-modal law which depends on: The distribution law of the new component (see point 1) and its minimal extreme law, which is asymmetrical, for an observation on 50 years. The statistical distribution at the time t of the residual mechanical performances of a group of supports making a local net, evaluated by non destructive methods. The non destructive evaluation is based on the measurements of physical variables (density, biological moisture content) and some descriptive variables from natural origins (diameter, knots, cracks...) and from accidental origins (diameter reduction, lightning cracks...). The statistical distribution at the time t is then obtained on the basis of a model of multivariate non destructive evaluation, generalized to the whole of species and treatments. This model is the other concrete goal to reach in this thesis. As a conclusion, the research demonstrates the influence and the interaction of the new pole grading (distribution at t0) on the modelisation of the distribution at ti (multivariate non destructive model). The data used for the mentioned modelisations come from a significant international database with a large amount of inspected wood poles and with studied cases. This database is the synthesis of about 15 years of research and development leaded by IBOIS-EPFL and its international partners. The probabilistic approaches are then validated by a huge database allowing thus to be directly exploitable. On this basis, all the standards dealing with the new poles and dealing with the controls and maintenances of a wooden pole networks, could be re-examined for a double gain: Concerning the economy: by increasing the capacity of the new poles profiting of an objective quality assurance, and by increasing the life time of the in-field pole, in knowing how to purge only the ones which are under the critical threshold of damage Concerning the reliability: by increasing the reliability of the network from the stage "new pole", by eliminating the weakest components, and by maintaining this reliability during all the life time of the network thanks to a cyclic preventive maintenance (every 5 to 8 years) and the replacement of only the weakened poles.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts

Loading

Related publications

Loading

Related publications (24)

Loading

Loading

Loading

Related concepts (25)

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function

Nondestructive testing

Nondestructive testing (NDT) is any of a wide group of analysis techniques used in science and technology industry to evaluate the properties of a material, component or system without causing damage

Number

A number is a mathematical object used to count, measure, and label. The original examples are the natural numbers 1, 2, 3, 4, and so forth. Numbers can be represented in language with number words.

This thesis is a contribution to multivariate extreme value statistics. The tail of a multivariate distribution function is characterized by its spectral distribution, for which we propose a new semi-parametric model based on mixtures of Dirichlet distributions. To estimate the components of this model, reversible jump Monte Carlo Markov chain and EM algorithms are developed. Their performances are illustrated on real and simulated data, obtained using new representations of the extremal logistic and Dirichlet models. In parallel with the estimation of the spectral distribution, extreme value statistic machinery requires the selection of a threshold in order to classify data as extreme or not. This selection is achieved by a new method based on heuristic arguments. It allows a selection independent of the dimension of the data. Its performance is illustrated on real and simulated data. Primal scientific interests behind a multivariate extreme value analysis reside in the estimation of quantiles of rare events and in the exploration of the dependence structure, for which the estimation of the spectral measure is a means rather than an end. These two issues are addressed. For the first, a Monte Carlo method is developed based on simulation of extremes. It is compared with classical and new methods of the literature. For the second one, an original conditional dependence analysis is proposed, which enlightens various aspects of the dependence structure of the data. Examples using real data sets are given. In the last part, the semi-parametric model and the presented methods are extended to spatial extremes. It is made possible by considering the spectral distribution as the distribution of a random probability, an original viewpoint adopted throughout this thesis. Classical multivariate extremes are extended to extremes of random measures. The application is illustrated on rainfall data in China.

The increasing interest in using statistical extreme value theory to analyse environmental data is mainly driven by the large impact extreme events can have. A difficulty with spatial data is that most existing inference methods for asymptotically justified models for extremes are computationally intractable for data at several hundreds of sites, a number easily attained or surpassed by the output of physical climate models or satellite-based data sets. This thesis does not directly tackle this problem, but it provides some elements that might be useful in doing so. The first part of the thesis contains a pointwise marginal analysis of satellite-based measurements of total column ozone in the northern and southern mid-latitudes. At each grid cell, the r-largest order statistics method is used to analyse extremely low and high values of total ozone, and an autoregressive moving average time series model is used for an analogous analysis of mean values. Both models include the same set of global covariates describing the dynamical and chemical state of the atmosphere. The results show that influence of the covariates is captured in both the ``bulk'' and the tails of the statistical distribution of ozone. For some covariates, our results are in good agreement with findings of earlier studies, whereas unprecedented influences are retrieved for two dynamical covariates. The second part concerns the frameworks of multivariate and spatial modelling of extremes. We review one class of multivariate extreme value distributions, the so-called Hüsler--Reiss model, as well as its spatial extension, the Brown--Resnick process. For the former, we provide a detailed discussion of its parameter matrix, including the case of degeneracy, which arises if the correlation matrices of underlying multivariate Gaussian distributions are singular. We establish a simplification for computing the partial derivatives of the exponent function of these two models. As consequence of the considerably reduced number of terms in each partial derivative, computation time for the multivariate joint density of these models can be reduced, which could be helpful for (composite) likelihood inference. Finally, we propose a new variant of the Brown--Resnick process based on the Karhunen--Loève expansion of its underlying Gaussian process. As an illustration, we use composite likelihood to fit a simplified version of our model to a hindcast data set of wave heights that shows highly dependent extremes.

Modern data storage systems are extremely large and consist of several tens or hundreds of nodes. In such systems, node failures are daily events, and safeguarding data from them poses a serious design challenge. The focus of this thesis is on the data reliability analysis of storage systems and, in particular, on the effect of different design choices and parameters on the system reliability. Data redundancy, in the form of replication or advanced erasure codes, is used to protect data from node failures. By storing redundant data across several nodes, the surviving redundant data on surviving nodes can be used to rebuild the data lost by the failed nodes if node failures occur. As these rebuild processes take a finite amount of time to complete, there exists a nonzero probability of additional node failures during rebuild, which eventually may lead to a situation in which some of the data have lost so much redundancy that they become irrecoverably lost from the system. The average time taken by the system to suffer an irrecoverable data loss, also known as the mean time to data loss (MTTDL), is a measure of data reliability that is commonly used to compare different redundancy schemes and to study the effect of various design parameters. The theoretical analysis of MTTDL, however, is a challenging problem for non-exponential real-world failure and rebuild time distributions and for general data placement schemes. To address this issue, a methodology for reliability analysis is developed in this thesis that is based on the probability of direct path to data loss during rebuild. The reliability analysis is detailed in the sense that it accounts for the rebuild times involved, the amounts of partially rebuilt data when additional nodes fail during rebuild, and the fact that modern systems use an intelligent rebuild process that will first rebuild the data having the least amount of redundancy left. Through rigorous arguments and simulations it is established that the methodology developed is well-suited for the reliability analysis of real-world data storage systems. Applying this methodology to data storage systems with different types of redundancy, various data placement schemes, and rebuild constraints, the effect of the design parameters on the system reliability is studied. When sufficient network bandwidth is available for rebuild processes, it is shown that spreading the redundant data corresponding to the data on each node across a higher number of other nodes and using a distributed and intelligent rebuild process will improve the system MTTDL. In particular, declustered placement, which corresponds to spreading the redundant data corresponding to each node equally across all other nodes of the system, is found to potentially have significantly higher MTTDL values than other placement schemes, especially for large storage systems. This implies that more reliable data storage systems can be designed merely by changing the data placement without compromising on the storage efficiency or performance. The effect of a limited network rebuild bandwidth on the system reliability is also analyzed, and it is shown that, for certain redundancy schemes, spreading redundant data across more number of nodes can actually have a detrimental effect on reliability. It is also shown that the MTTDL values are invariant in a large class of node failure time distributions with the same mean. This class includes the exponential distribution as well as the real-world distributions, such as Weibull or gamma. This result implies that the system MTTDL will not be affected if the failure distribution is changed to a corresponding exponential one with the same mean. This observation is also of great importance because it suggests that the MTTDL results obtained in the literature by assuming exponential node failure distributions may still be valid for real-world storage systems despite the fact that real-world failure distributions are non-exponential. In contrast, it is shown that the MTTDL is sensitive to the node rebuild time distribution. A storage system reliability simulator is built to verify the theoretical results mentioned above. The simulator is sufficiently complex to perform all required failure events and rebuild tasks in a storage system, to use real-world failure and rebuild time distributions for scheduling failures and rebuilds, to take into account partial rebuilds when additional node failures occur, and to simulate different data placement schemes and compare their reliability. The simulation results are found to match the theoretical predictions with high confidence for a wide range of system parameters, thereby validating the methodology of reliability analysis developed.