Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A cost-benefit approach for the evaluation of prognostics-updated maintenance strategies in complex dynamic systems

The implementation of maintenance strategies which integrate online condition data has the potential to increase availability and reduce maintenance costs. Prognostics techniques enable the implementation of these strategies through up-to-date remaining useful life estimations. However, a cost-benefit assessment is necessary to verify the scale of potential benefits of condition-based maintenance strategies and prognostics for a given application. The majority of prognostics applications focus on the evaluation of a specific failure mode of an asset. However, industrial systems are comprised of different assets with multiple failure modes, which in turn, work in cooperation to perform a system level function. Besides, these systems include time-dependent events and conditional triggering events which cause further effects on the system. In this context not only are the system-level prognostics predictions challenging, but also the cost-benefit analysis of condition-based maintenance policies. In this work we combine asset prognostics predictions with temporal logic so as to obtain an up-to-date system level health estimation. We use asset level and system level prognostics estimations to evaluate the cost-effectiveness of alternative maintenance policies. The application of the proposed approach enables the adoption of conscious trade-off decisions between alternative maintenance strategies for complex systems. The benefits of the proposed approach are discussed with a case study from the power industry

A robust hierarchical clustering for georeferenced data

The detection of spatially contiguous clusters is a relevant task in geostatistics since near located observations might have similar features than distant ones. Spatially compact groups can also improve clustering results interpretation according to the different detected subregions. In this paper, we propose a robust metric approach to neutralize the effect of possible outliers, i.e. an exponential transformation of a dissimilarity measure between each pair of locations based on non-parametric kernel estimator of the direct and cross variograms (Fouedjio, 2016) and on a different bandwidth identification, suitable for agglomerative hierarchical clustering techniques applied to data indexed by geographical coordinates. Simulation results are very promising showing very good performances of our proposed metric with respect to the baseline ones. Finally, the new clustering approach is applied to two real-word data sets, both giving locations and top soil heavy metal concentrations

Synthesis, Characterization and Electrocatalytic Activity of Bi- and Tri-metallic Pt-Based Anode Catalysts for Direct Ethanol Fuel Cells

Three Pt-based anode catalysts supported on Vulcan XC-72R (VC) were prepared by using a modified polyol process. These materials were characterized and tested by X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF) and Transmission Electron Microscopy (TEM). XRD and TEM analysis indicated that especially the ternary anode catalysts consisted of uniform nanosized particles with sharp distribution. The Pt lattice parameter was smaller, in the ternary PtSnIr catalyst whereas it increased with the addition of Sn and Rh, in the corresponding binary and ternary catalysts. Cyclic voltammetry (CV) measurements showed that Sn, Ir and Rh may act as promoter of Pt enhancing ethanol electro-oxidation activity. It was found that the direct ethanol fuel cell (DEFC) performances were significantly improved with these modified anode catalysts. This effect on the DEFC performance is attributed to the so-called bi-tri-functional mechanism and to the electronic interaction between Pt and additives. The performance increased significantly with the temperature. However, it was also possible to observe some decay with time for all catalysts due to the formation of surface poisons, probably consisting in CO-like species. At 60 °C, the PtSnIr catalyst showed the best performance, as a result of a proper morphology and promoting effectFil: D'Urso, C.. Centro Nazionale della Ricerca. ITAE; ItaliaFil: Bonesi, Alejandro Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Triaca, Walter Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Castro Luna Berenguer, Ana Maria del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Baglio, V.. Centro Nazionale della Ricerca. ITAE; Italia; ItaliaFil: Aricò, A. S.. Centro Nazionale della Ricerca. ITAE; Italia; Itali

The Central Laser Facility at the Pierre Auger Observatory

The Central Laser Facility is located near the middle of the Pierre Auger Observatory in Argentina. It features a UV laser and optics that direct a beam of calibrated pulsed light into the sky. Light scattered from this beam produces tracks in the Auger optical detectors which normally record nitrogen fluorescence tracks from cosmic ray air showers. The Central Laser Facility provides a "test beam" to investigate properties of the atmosphere and the fluorescence detectors. The laser can send light via optical fiber simultaneously to the nearest surface detector tank for hybrid timing analyses. We describe the facility and show some examples of its many uses.Comment: 4 pages, 5 figures, submitted to 29th ICRC Pune Indi

Internal alignment and position resolution of the silicon tracker of DAMPE determined with orbit data

The DArk Matter Particle Explorer (DAMPE) is a space-borne particle detector designed to probe electrons and gamma-rays in the few GeV to 10 TeV energy range, as well as cosmic-ray proton and nuclei components between 10 GeV and 100 TeV. The silicon-tungsten tracker-converter is a crucial component of DAMPE. It allows the direction of incoming photons converting into electron-positron pairs to be estimated, and the trajectory and charge (Z) of cosmic-ray particles to be identified. It consists of 768 silicon micro-strip sensors assembled in 6 double layers with a total active area of 6.6 m$^2$. Silicon planes are interleaved with three layers of tungsten plates, resulting in about one radiation length of material in the tracker. Internal alignment parameters of the tracker have been determined on orbit, with non-showering protons and helium nuclei. We describe the alignment procedure and present the position resolution and alignment stability measurements

INFN Camera demonstrator for the Cherenkov Telescope Array

The Cherenkov Telescope Array is a world-wide project for a new generation of ground-based Cherenkov telescopes of the Imaging class with the aim of exploring the highest energy region of the electromagnetic spectrum. With two planned arrays, one for each hemisphere, it will guarantee a good sky coverage in the energy range from a few tens of GeV to hundreds of TeV, with improved angular resolution and a sensitivity in the TeV energy region better by one order of magnitude than the currently operating arrays. In order to cover this wide energy range, three different telescope types are envisaged, with different mirror sizes and focal plane features. In particular, for the highest energies a possible design is a dual-mirror Schwarzschild-Couder optical scheme, with a compact focal plane. A silicon photomultiplier (SiPM) based camera is being proposed as a solution to match the dimensions of the pixel (angular size of ~ 0.17 degrees). INFN is developing a camera demonstrator made by 9 Photo Sensor Modules (PSMs, 64 pixels each, with total coverage 1/4 of the focal plane) equipped with FBK (Fondazione Bruno Kessler, Italy) Near UltraViolet High Fill factor SiPMs and Front-End Electronics (FEE) based on a Target 7 ASIC, a 16 channels fast sampler (up to 2GS/s) with deep buffer, self-trigger and on-demand digitization capabilities specifically developed for this purpose. The pixel dimensions of $6\times6$ mm$^2$ lead to a very compact design with challenging problems of thermal dissipation. A modular structure, made by copper frames hosting one PSM and the corresponding FEE, has been conceived, with a water cooling system to keep the required working temperature. The actual design, the adopted technical solutions and the achieved results for this demonstrator are presented and discussed.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

Robust DTW-based entropy fuzzy clustering of time series

Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures