Experimental Aspects of Highly Accelerated Life Tests

Experimental aspects of High Accelerated Life Tests (HALT) are presented. A statistical analysis of test results which allows to define the different characteristic areas of a product (operating and destruction limits) is given. In particular, some tests on electronic board using thermal and vibration stresses have been investigated

Synthesis of leachate from the Al Hoceima controlled landfill and characterization (Morocco , North of Africa)

The leachate contains various types of organic and inorganic matter which can pollute aquatic environments if they are not properly treated. The choice and the efficiency of treatment techniques depend on the characteristics of the leachate, these characteristics change from one region to another and according to time and seasons. The objective of this study is to prepare synthetic leachate based on the characteristics of real leachate (the case of the Al Hoceima controlled landfill), in order to carry out tests of treatability of this synthetic leachate by controlling the different analysis parameters, in order to be able to choose the suitable treatment technique for the type of leachate to be treated. In our case for the leachate from the controlled landfill of Al Hoceima city, the characteristics are very variable; COD varies from 12365 to 33600 mg(O2)/L, ammonium varies between 1921 and 4060 mg/L, chloride from 4660 up to 7196 mg/L. In fact, this solution was made from organic (CH3CO2H, CH3CO2Na) and inorganic compounds (MgSO4, CaCl2, Ca(OH)2, NH4Cl, KOH, MgCl2), the various analyzes of this solution gave values comparable to the real solution

Uncertainty Evaluation for Testing

International audienc

Detection of Faults and Drifts in the Energy Performance of a Building Using Bayesian Networks

Despite improved commissioning practices, malfunctions or degradation of building systems still contribute to increase up to 20% the energy consumption. During operation and maintenance stage, project and building technical managers need appropriate methods for the detection and diagnosis of faults and drifts of energy performances in order to establish effective preventive maintenance strategies. This paper proposes a hybrid and multilevel fault detections and diagnosis (FDD) tool dedicated to the identification and prioritization of corrective maintenance actions helping to ensure the energy performance of buildings. For this purpose, we use dynamic Bayesian networks (DBN) to monitor the energy consumption and detect malfunctions of building equipment and systems by considering both measured occupancy and the weather conditions (number of persons on site, temperature, relative humidity (RH), etc.). The hybrid FDD approach developed makes possible the use of both measured and simulated data. The training of the Bayesian network for functional operating mode relies on on-site measurements. As far as dysfunctional operating modes are concerned, they rely mainly on knowledge extracted from dynamic thermal analysis simulating various operational faults and drifts. The methodology is applied to a real building and demonstrates the way in which the prioritization of most probable causes can be set for a fault affecting energy performance. The results have been obtained for a variety of simulated situations with faults deliberately injected, such as increase in heating preset temperature and deterioration of the transmission coefficient of the building\u27s glazing. The limitations of the methodology are discussed and are translated in terms of the ability to optimize the experiment design, control period, or threshold adjustment on the control charts used

Reliability and availability estimation of a photovoltaic system using Petri networks

Many photovoltaic (PV) systems are nowadays installed all around the world. However, the reliability and the availability estimation of photovoltaic systems have not been received great attention from researchers. Reliability and availability are important consideration in the life-cycle of such systems. This paper presents a methodology for estimating the reliability and the availability of a photovoltaic system using Petri networks. Each component - module, wires and inverter - is detailed in Petri networks and several laws are used in order to determine the reliability and system availability. The degradation function of each componenthas been taken into account. Results show that Petri networks simplify the reliability and availability modeling and analysis

A Bayesian Regularized Artificial Neural Network for Up-Scaling Wind Speed Profile

Maximizing gains from wind energy potential is the principle objective of the wind power sector. Consequently, wind tower size is radically increasing. However, choosing an appropriate wind turbine for a selected site requires having an accurate estimation of vertical wind profile. This is also imperative from the cost and maintenance strategy point of view. Installing tall towers or other expensive devices such as LIDAR or SODAR raises the costs of a wind power project. In this work, we aim to investigate the ability of a Neural Network trained using the Bayesian Regularization technique to estimate wind speed profile up to a height of 100m based on knowledge of wind speed at lower heights. Results show that the proposed approach can achieve satisfactory predictions and prove the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights