In this paper, a methodology is presented for estimating the lifetime of a photovoltaic (PV) module. Designers guarantee an acceptable level of power (80% of the initial power) up to 25 yr for solar panels without having sufficient feedback to validate this lifetime. Accelerated life testing (ALT) can be carried out in order to determine the lifetime of the equipment. Severe conditions are used to accelerate the ageing of components and the reliability is then deduced in normal conditions, which are considered to be stochastic rather than constant. Environmental conditions at normal operations are simulated using IEC 61725 standard and meteorological data. The mean lifetime of a crystalline-silicon photovoltaic module that meets the minimum power requirement is estimated. The main results show the influence of lifetime distribution and Peck model parameters on the estimation of the lifetime of a photovoltaic module.

This article presents a method developed for carrying out the energy production estimation considering the energy losses in different components of a photovoltaic (PV) system and its downtime effect. The studied system is a grid-connected photovoltaic system including PV modules, wires, and inverter. PV systems are sensitive to environmental conditions (UV radiation, temperature, humidity) and all components are subjected to electrical losses. The proposed method allows obtaining the production of photovoltaic system and its availability during a specified period using meteorological data. The calculation of the production takes into account the downtime periods when no energy is delivered in the grid during this period. The time-to-failure and the time-to-repair of photovoltaic system are considered following a Weibull distribution. This method permits to have a best estimation of the production throughout the lifetime of the photovoltaic system.

In the building domain, components or equipment are often subjected to severe environmental conditions. In order to predict the reliability and the life-time of such equipment, accelerated life testing can be carried out. Severe conditions are applied to accelerate the ageing of the components and the reliability at nominal conditions is then deduced considering that these nominal conditions are not constant but stochastic. In this paper, the accelerated life testing of photovoltaic modules is carried out at severe module temperature levels. The module power losses are monitored and the limit state is determined when a threshold power is reached. The stochastic data and the reliability are simulated during a period of twenty years. Finally, the life time of the component is evaluated.

In the face of the COVID-19 (Coronavirus Disease 2019) pandemic, the World Health Organization (WHO) has urged countries to test the population more widely. Clinical laboratories have been confronted with a huge demand for testing and have had to make urgent preparations for staff training, to establish new analytical processes, reorganize the workspace, and stock up on specific equipment and diagnostic test kits. The reliability of SARS-Cov-2 test results is of critical importance, given the impact it has on patient care and the management of the health crisis. A review of the literature available for the period leading up to and including June 2020 on the reliability of SARS-Cov-2 (Severe Acute Respiratory Syndrome Coronavirus) detection methods using real-time RT PCR (Reverse Transcription - Polymerase Chain Reaction) brings together the primary factors teams of scientists claim or demonstrate to affect the reliability of results. A description is given of the RT-PCR testing method, followed by a presentation of the characteristics and validation techniques used. A summary of data from the literature on the reliability of tests and commercial kits for SARS-Cov-2 detection, including current uncertainties with regard to the molecular targets selected and genetic diversity of SARS-Cov-2 is provided. The limitations and perspectives are then discussed in detail in the light of the bibliographic data available. Many questions have been asked that still remain unanswered. The lack of knowledge about this novel virus, which appeared at the end of 2019, has a significant impact on the technical capacity to develop reliable, rapid and practical tools for its detection.