Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

Robust weighted aggregation of expert opinions in futures studies

Expert judgments are widespread in many fields, and the way in which they are collected and the procedure by which they are aggregated are considered crucial steps. From a statistical perspective, expert judgments are subjective data and must be gathered and treated as carefully and scientifically as possible. In the elicitation phase, a multitude of experts is preferable to a single expert, and techniques based on anonymity and iterations, such as Delphi, offer many advantages in terms of reducing distortions, which are mainly related to cognitive biases. There are two approaches to the aggregation of the judgments given by a panel of experts, referred to as behavioural (implying an interaction between the experts) and mathematical (involving non-interacting participants and the aggregation of the judgments using a mathematical formula). Both have advantages and disadvantages, and with the mathematical approach, the main problem concerns the subjective choice of an appropriate formula for both normalization and aggregation. We propose a new method for aggregating and processing subjective data collected using the Delphi method, with the aim of obtaining robust rankings of the outputs. This method makes it possible to normalize and aggregate the opinions of a panel of experts, while modelling different sources of uncertainty. We use an uncertainty analysis approach that allows the contemporaneous use of different aggregation and normalization functions, so that the result does not depend on the choice of a specific mathematical formula, thereby solving the problem of choice. Furthermore, we can also model the uncertainty related to the weighting system, which reflects the different expertise of the participants as well as expert opinion accuracy. By combining the Delphi method with the robust ranking procedure, we offer a new protocol covering the elicitation, the aggregation and the processing of subjective data used in the construction of Delphi-based future scenarios. The method is very flexible and can be applied to the aggregation and processing of any subjective judgments, i.e. also those outside the context of futures studies. Finally, we show the validity, reproducibility and potential of the method through its application with regard to the future of Italian families

Modeling the Effects of Maintenance on the degradation of a Water-feeding Turbo-pump of a Nuclear Power Plant

International audienceThis work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation. The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts

A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction

Identification of Contradictory Patterns in Experimental Datasets for the Development of Models for Electrical Cables Diagnostics

International audienceThe state of health of an electrical cable may be difficult to know, without destructive or very expensive tests. To overcome this, partial discharge (PD) measurements have been proposed as a relatively economic and simple-to-apply experimental technique for retrieving information on the state of health of an electrical cable. The retrieval is based on a relationship between PD measurements and the health of the cable. Given the difficulties in capturing such relationship by analytical models, empirical modeling techniques based on experimental data have been propounded. In this view, a set of PD measurements have been collected by Enea Ricerca sul Sistema Elettrico-ERSE during past campaigns, for building a diagnostic system of electrical cable health state. These experimental data may contain contradictory information which remarkably reduces the performance of the state classifier, if not a priori identified and possibly corrected. In the present paper, a novel technique based on the Adaboost algorithm is proposed for identifying contradictory PD patterns within an a priori analysis aimed at improving the diagnostic performance. Adaboost is a bootstrap-inspired, ensemble-based algorithm which has been effectively used for addressing classification problems

Risk assessment of a bulk cryogenic tank: Beyond the Leak-Before-Break criterion

International audienceThe increase in the size and production capacity of air separation plants has boosted the need of developing methodologies to properly assess the risk related to major releases of liquefied gas. In this respect, the Leak-Before-Break (LBB) assessment is currently adopted to demonstrate the safety of the structures containing liquefied gas, under the assumption that the tank is always operated in nominal conditions. This assumption is questioned in this paper, which proposes a new methodology for the assessment of the risks related to cryogenic tank catastrophic rupture. The methodology provides a comprehensive understanding of the issues associated to the worst case rupture scenario: from the investigation of the causes of the undesirable operating conditions up to the analysis of the associated structural consequences, within a probabilistic framewo

A reinforcement learning framework for optimal operation and maintenance of power grids

International audienc

Meiofauna and nematode diversity in some Mediterranean subtidal areas of the Adriatic and Ionian Sea

Sediments of three different subtidal areas (15-705 m depth) of the Italian coasts (Manfredonia, Brindisi and Gallipoli) were investigated to study meiofauna and nematode composition. The nematodes were identified to the genus level and their abundances compared using multivariate analysis. Our data showed an evident depth gradient in meiofauna abundance: the shallowest sites had more diverse and abundant meiobenthic communities than the deeper ones. Nematodes were the dominant taxon (83-100%) at all sites, followed by Copepoda (0.5-8%). Sabatieria, Astomonema, Dorylaimopsis, Terschellingia and Daptonema were among the dominant nematode genera in the three areas. Nematode genus H’ diversities were not significantly dissimilar, though at community level some differences were detected among the study areas. The greatest differences were observed in the comparison of the communities from Manfredonia and Gallipoli. Furthermore, there was a difference between shallow (Astomonema, Dorylaimopsis, Sabatieria and Terschellingia)

A Bootstrapped Modularised method of Global Sensitivity Analysis applied to Probabilistic Seismic Hazard Assessment

Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametri-zations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those.The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations

Role of PKC in the Regulation of the Human Kidney Chloride Channel ClC-Ka

The physiological role of the renal ClC-Ka/ClC-K1 channels is to confer a high Cl- permeability to the thin Ascending Limb of Henle (tAL), which in turn is essential for establishing the high osmolarity of the renal medulla that drives water reabsorption from collecting ducts. Here, we investigated by whole-cell patch-clamp measurements on HEK293 cells co-expressing ClC-Ka (tagged with GFP) and the accessory subunit barttin (tagged with m-Cherry) the effect of a natural diuretic extract from roots of Dandelion (DRE), and other compounds activating PKC, such as ATP, on ClC-Ka activity and its membrane localization. Treatment with 400 µg/ml DRE significantly inhibited Cl- currents time-dependently within several minutes. Of note, the same effect on Cl- currents was obtained upon treatment with 100 µM ATP. Pretreatment of cells with either the intracellular Ca2+ chelator BAPTA-AM (30 μM) or the PKC inhibitor Calphostin C (100 nM) reduced the inhibitory effect of DRE. Conversely, 1 µM of phorbol meristate acetate (PMA), a specific PKC activator, mimicked the inhibitory effect of DRE on ClC-Ka. Finally, we found that pretreatment with 30 µM Heclin, an E3 ubiquitin ligase inhibitor, did not revert DRE-induced Cl- current inhibition. In agreement with this, live-cell confocal analysis showed that DRE treatment did not induce ClC-Ka internalization. In conclusion, we demonstrate for the first time that the activity of ClC-Ka in renal cells could be significantly inhibited by the activation of PKC elicited by classical maneuvers, such as activation of purinergic receptors, or by exposure to herbal extracts that activates a PKC-dependent pathway. Overall, we provide both new information regarding the regulation of ClC-Ka and a proof-of-concept study for the use of DRE as new diuretic