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E. Barca, D. E. Bruno, A. Lay-Ekuakille, S. Maggi, G. Passarella
Heuristic Rules for a Reliable Variogram Parameter Tuning

The variogram incorporates the basic information for the geostatistical analysis of spatial data, mak- ing it one of the most popular and widespread approaches for the evaluation and assessment of environmental data. Fitting an experimental variogram with a proper model, capable of catching the main characteristics of the sampled data, is a critical stage of spatial analysis. This stage is generally carried out using a semi-automatic approach, with different levels of user’s contribution. Once a model and the related starting parameter values have been defined, they can be refined using a trial-and-error strategy and the impact of such changes can be evaluated through several calibration indices. In this paper, we present an overview of the main indices used for the refinement of the variogram model parameters, highlighting the specific information provided by each of them together with a few heuristic strategies for driving the trial-and-error refinement process.

Mike Gerdes, Diego Galar, Dieter Scholz
Automated Parameter Optimization for Feature Extraction for Condition Monitoring

Pattern recognition and signal analysis can be used to support and simplify the monitoring of complex aircraft systems. For this purpose, information must be extracted from the gathered data in a proper way. The parameters of the signal analysis need to be chosen specifically for the monitored system to get the best pattern recognition accuracy. An optimization process to find a good parameter set for the signal analysis has been developed by the means of global heuristic search and optimization. The computed parameters deliver slightly (one to three percent) better results than the ones found by hand. In addition it is shown that not a full set of data samples is needed. It is also concluded that genetic optimization shows the best performance.

Christian Laurano, Giacomo Leone, Michele Zanoni
Outage Severity Analysis on Italian Overhead Transmission Lines from a Regional Perspective

Power transmission lines represent the core of the High Voltage Network since they are responsible for the transport of the electrical energy from the generation power plants to the electrical substations. In this paper, an analysis of the outages occurred to the Italian Overhead Transmission Lines (OHTLs) from 2008 to 2015 is carried out. A new simple and effective reliability index, namely the Severity Factor, is introduced in order to prioritize the most relevant outage causes. The analysis has been performed focusing on the geographical distribution of the OHTLs. The obtained results have shown that the impact of the different outage causes on the OHTLs reliability is generally not uniform across the country but depends on the considered region. Further, for each analyzed region, the voltage levels more prone to failure have been determined. It can be concluded that the proposed methodology, thanks to the introduction of the Severity Factor, is a useful and effective tool for the identification of the transmission network criticalities and the enhance of the related maintenance activities.

Marcantonio Catelani, Lorenzo Ciani, Giulia Guidi, Matteo Venzi
Parameter estimation methods for failure rate distributions

The main part of the paper deals with the parameter estimation methods that used to determine the best-fitting distribution for a set of data collected during testing or field operations. The methods considered in that case are Least Square Estimation and Maximum Likelihood Estimation. Finally, two different types of field data such as failure times achieved with testing of electronic board for automatic control and with accelerated testing on electronic components were analyzed to find which distribution fits better the data.

Marcantonio Catelani, Lorenzo Ciani, Matteo Venzi
Credible Improvement Potential index for Reliability Importance assessment

System reliability represents a key performance of modern products and nowadays being reliable is one of the most important requirements, in particular in the industrial field.
This paper is focused on the reliability improvement of complex systems containing redundant architectures through Reliability Importance (RI): starting from the single component reliability, RI procedures are used to evaluate their impact on the whole system.
This practice is particularly helpful during the design stage of complex systems since it allows design engineers to have reliability feedbacks before the realization of the product in order to focus the efforts on the components that have the greatest effect on the whole reliability performance.
The paper is organized as follows: the first section describes the Credible Improvement Potential (CIP) that is the most suitable RI metric for our purpose. The second section, instead, shows the application of the method on a generic complex system containing standby redundant blocks.

Giacomo Leone, Loredana Cristaldi
A Nonlinear Predictor for the Supervision of Photovoltaic Strings Performances

In this paper, a nonlinear predictor of the electrical power produced by a PV string is proposed. The first phase of the approach is the training of the predictor, during which four characteristic parameters are determined. Such coefficients are representative of the string under study and define its electrical signature (identikit). Once trained the model, when new monitoring data are available, the mismatch between the forecasted and measured electrical power can be assumed as a reliable marker of the performances of the string, since the greater the mismatch, the worse the string efficiency. The analysis of the forecasting error, therefore, enables the detection of losses of energy production. In particular, a strength of the proposed approach is the possibility to distinguish the losses due to aging phenomena from the losses due to the dust or dirt accumulation. The method has been tested and validated for a real case study and the obtained results are presented in the paper.

Ting Lei, Marco Faifer, Roberto Ottoboni, Sergio Toscani
Towards a Novel Approach to the On-Line Diagnosis of the Instrument Transformer

The conventional instrument transformers (CITs) are still widely used in the power network, thanks to their high reliability, insulation capability and low drift over time and temperature. Their traditional reliability is very often used as justification for skipping a periodical calibration, which requires putting off-line the CIT, thus implying a very complex and expensive procedure. For this reason, in the last years, a growing interest has been addressed towards the diagnostic/calibration methods based on on-line procedure. The typical approach is based on the frequency response analysis that permits, under sinusoidal conditions, to detect possible deterioration of the behavior of CIT. Anyway, the real interest is to check the CIT fleet already installed and operating on the grid without requiring the disconnection from the grid. As well known the actual voltage of the grid features a not negligible harmonic distortion that combined with the intrinsically non-linearity of the CTI reduces the feasibility of the on-line diagnostic methods based on the frequency response method. This paper proposed a novel approach to the on-line diagnosis of CIT based on a non-linear simplified Volterra model of the CIT. This opens the way to a different approach to the in-site characterization process of a CIT based on the actual voltage of the grid and thus not requiring the disconnection of the CIT from the grid.

Giacomo Leone, Loredana Cristaldi, Simone Turrin
A Data-Driven Prognostic Approach Based on Sub-Fleet Knowledge Extraction

In this paper, a data-driven prognostic algorithm for the estimation of the Remaining Useful Life (RUL) of a product is proposed. It is based on the acquisition and exploitation of run-to-failure data of homogeneous products, in the followings referred as fleet of products. The peculiar feature of the proposal is that the products composing the fleet are not strictly required to belong to the same system or plants, since the only constraint is that they are characterized by similar operating conditions (f.i. installed in the same region or operating in the same industrial application). The algorithm, indeed, is able to detect the set of products (sub-fleet of products) showing highest usage and degradation pattern similarity with the one under study and exploits the related monitoring data for a reliable prediction of the RUL, resulting in a potential tool for an effective Predictive Maintenance (PdM) strategy.

Alessia Bramanti, Alessandro Sciva, Giuseppe Campobello, Pier Paolo Capra, Silvia Marino, Placido Bramanti, Nicola Donato
A compact monitoring system for patients affected by neurodegenerative diseases

In this paper we report about the development of a compact monitoring system for people affected by neurodegenerative diseases as Parkinson and Alzheimer. The electronic interface is based on a Linkit ONE development board, and it is equipped with an array of up to four ADXL345 accelerometers. By considering the main symptoms of Parkinson subjects, it is possible to monitor tremors/movements and accidental falls of people wearing the system and to send data to a supervisor by Wi-Fi, Bluetooth and GSM connections. Furthermore, the geo-tagging functions, developed by means of a GPS feature, already equipped in the board, allow the monitoring of the movements and the position of Alzheimer subjects, avoiding the lost for memory failures.

Paolo Parenti, Marco Camagni, Massimiliano Annoni
Surface quality monitoring in micromilling: a preliminary investigation on microfeatures

Process monitoring plays a key role in industrial machining operations such as turning, milling and grinding, for assuring part quality and long lifespan of cutting tools to reduce production costs. When the part dimensions are scaled down to submillimeter range, process monitoring becomes essential but harder to implement. This work evaluates the possibility to monitor the surface quality of micromilled parts by means of cutting-related signals, such as Cutting Forces and Acoustic Emission. Micromilling of cylindrical pins with 0.8 mm diameter and aspect ratio of 1.875 has been studied, representing a significant case for injection molds. This manufacturing task is challenging because both the dimensional/geometrical accuracy and the finishing quality of the part have to be maximized to guarantee the final part functionalities. On the other side, the direct measurement of these characteristics on the final parts is challenging considering the state-of-the-art metrology systems. This fact motivates the adoption of indirect monitoring approaches, that can estimate the part quality in alternative ways. A design of experiment approach (DoE) together with Analysis of Variance (ANOVA), have been adopted to assess the statistical relationship between the monitored signals and the quality indexes of interest, such as the average surface roughness of the parts. The emerged correlations between the achievable surface roughness and process parameters, together with the existing correlation of micro cutting forces and acoustic emission with process parameters, sustain the feasibility of using these signals for implementing advanced monitoring and control schemes of the micro cutting operation.

Page 326 of 977 Results 3251 - 3260 of 9762