Unified Evaluation Framework for Stochastic Algorithms Applied to Remaining Useful Life Prognosis Problems

A unified evaluation framework for stochastic tools is developed in this paper. Firstly, we provide a set of already existing quantitative and qualitative metrics that rate the relevant aspects of the performance of a stochastic prognosis algorithm. Secondly, we provide innovative guidelines to detect and minimize the effect of side aspects that interact on the algorithms’ performance. Those aspects are related with the input uncertainty (the uncertainty on the data and the prior knowledge), the parametrization method and the uncertainty propagation method. The proposed evaluation framework is contextualized on a Lithium-ion battery Remaining Useful Life prognosis problem. As an example, a Particle Filter is evaluated. On this example, two different data sets taken from NCA aged batteries and two semi-empirical aging models available in the literature fed up the Particle Filter under evaluation. The obtained results show that the proposed framework gives enough details to take decisions about the viability of the chosen algorithm

Application Dependent End-of-Life Threshold Definition Methodology for Batteries in Electric Vehicles

The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-oflife threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations

A Data-Driven Health Assessment Method for Electromechanical Actuation Systems

The design of health assessment applications for the electromechanical actuation system of the aircraft is a challenging task. Physics-of-failure models involve non-linear complex equations which are further complicated at the system-level. Data-driven techniques require run-to-failure tests to predict the remaining useful life. However, components are not allowed to run until failure in the aerospace engineering arena. Besides, when adding new monitoring elements for an improved health assessment, the airliner sets constraints due to the increased cost and weight. In this context, the health assessment of the electromechanical actuation system is a challenging task. In this paper we propose a data-driven approach which estimates the health state of the system without runto-failure data and limited health information. The approach combines basic reliability theory with Bayesian concepts and obtained results show the feasibility of the technique for asset health assessment

A data-driven health assessment method for electromechanical actuation systems

The design of health assessment applications for the electromechanical actuation system of the aircraft is a challenging task. Physics-of-failure models involve non-linear complex equations which are further complicated at the system-level. Data-driven techniques require run-to-failure tests to predict the remaining useful life. However, components are not allowed to run until failure in the aerospace engineering arena. Besides, when adding new monitoring elements for an improved health assessment, the airliner sets constraints due to the increased cost and weight. In this context, the health assessment of the electromechanical actuation system is a challenging task. In this paper we propose a data-driven approach which estimates the health state of the system without run-to-failure data and limited health information. The approach combines basic reliability theory with Bayesian concepts and obtained results show the feasibility of the technique for asset health assessment

FPGA-Based Degradation and Reliability Monitor for Underground Cables

The online Remaining Useful Life (RUL) estimation of underground cables and their reliability analysis requires obtaining the cable failure time probability distribution. Monte Carlo (MC) simulations of complex thermal heating and electro-thermal degradation models can be employed for this analysis, but uncertainties need to be considered in the simulations, to produce accurate RUL expectation values and confidence margins for the results. The process requires performing large simulation sets, based on past temperature or load measurements and future load predictions. Field Programmable Gate Arrays (FPGAs) permit accelerating simulations for live analysis, but the thermal models involved are complex to be directly implemented in hardware logic. A new standalone FPGA architecture has been proposed for the fast and on-site degradation and reliability analysis of underground cables, based on MC simulation, and the effect of load uncertainties on the predicted cable End Of Life (EOL) has been analyzed from the results

Prognostics & health management methods & tools for transformer condition monitoring in smart grids

Power transformers are critical assets for the correct and reliable operation of the power grid. However, the use of power transformers in the context of smart grids creates new challenges for efficient lifetime management and maintenance planning. The use of intermittent sources of energy and dynamic loads increases the sources of uncertainty and causes non-linear operation dynamics. In addition, the increased use of probabilistic forecasting models for the estimation of influential parameters such as temperature or load, influences the uncertainty associated with the transformer lifetime estimation. These variable operation mechanisms influence the operation and lifetime planning of power transformers. Accordingly, this paper presents a novel probabilistic health state estimation framework to improve the lifetime management of power transformers operated in smart grids through the integration of probabilistic forecasting models with Monte Carlo based Bayesian filtering methods

Application des réseaux de neurones à l'identification d'un axe de machine-outil

The machining-tools hâve been studied according to the évolution of the manufacturing process requirements. Nowadays, new construction materials and structures are being evaluated as well as the optimisation of the numeric control. This thesis tries to answer whether the use of the Artificial Neural Networks to compensate the résonances and to adapt itself to the variations in the machine is feasible. To be accurate, we have studied the modelling of the axis, mechanical and electrical parts included, with its imperfections and the application of the Artificial Neural Networks to identify those nonlinear effects. We hâve also made the comparison with the traditional linear identification methods.Les machines-outils ont été l'objet des études au fur et à mesure que les entreprises voulaient augmenter la productivité. De nos jours, les aspects qui sont traités sont les nouveaux matériaux pour la partie mécanique d'une part et l'amélioration du contrôle numérique d'autre part. Cette thèse essaie de répondre à la question sur la viabilité de l'usage des Réseaux de Neurones Artificiels à la compensation des résonances et l'autoréglage. Plus exactement nous avons étudié la modélisation de l'axe, la mécanique aussi comme l'électrique, avec ses imperfections et l'application des réseaux de neurones pour identifier ces phénomènes non linéaires. Nous avons aussi profité pour faire le parallèle avec les méthodes traditionnelles d'identification linéaire