Loccioni Group

Rosora, Italy

Loccioni Group

Rosora, Italy
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Rossetti D.,Marche Polytechnic University | Squartini S.,Marche Polytechnic University | Collura S.,Loccioni Group | Zhang Y.,University of Lincoln
Proceedings of the 2016 17th International Conference on Mechatronics - Mechatronika, ME 2016 | Year: 2016

In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training. In this paper, the authors propose two alternative strategies for improving the regression performance by using heterogeneous data, i.e. data coming from diverse environments with respect to the one taken as reference for testing. These strategies are based on a standard machine learning algorithm, i.e. the Artificial Neural Network (ANN). The employed data came from measurements in industrial plants for energy production through the combustion of coal powder. The powder is transported in air within ducts and its size is detected by means of Acoustic Emissions (AE) produced by the impact of powder on the inner surface of the duct. The estimation of powder size distribution from AE signals is the task addressed in this work. Computer simulations show how the proposed strategies achieve a relevant improvement of regression performance with respect to the standard approach, using ANN directly on the dataset related to the reference plant. © 2016 Czech Technical University in Prague.


Rossetti D.,Marche Polytechnic University | Zhang Y.,University of Lincoln | Squartini S.,Marche Polytechnic University | Collura S.,Loccioni Group
Proceedings of the 2016 17th International Conference on Mechatronics - Mechatronika, ME 2016 | Year: 2016

The present work proposes a new technique for bearing fault classification that combines time-frequency analysis with image processing. This technique uses vibration signals from bearing housings to detect bearing conditions and classify the faults. The signals are decomposed by means of Empirical Mode Decomposition (EMD) and Principal Components Analysis (PCA) in order to obtain the principal components that characterize the original signal. The spectrogram is calculated for each component by means of the Short Time Fourier Transform (STFT) to obtain an image that represents the time-frequency relationship of the main components of the analyzed signal. Furthermore, Image Moments are extracted from the spectrogram images of principal components in order to get an array of features for each signal that can be handled by the classification algorithm. Finally, the classification is performed using a standard machine learning technique, i.e. Support Vector Machine (SVM), in the proposed technique. The dataset used in this work include data collected for various rotating speeds and loads, in order to obtain a set of different operating conditions, by a Roller Bearing Faults Simulator. The results have shown that the developed technique provides classification effectively, with a single classifier, of bearing faults characterized by different rotating speeds and different loads. © 2016 Czech Technical University in Prague.


Mariani A.,Loccioni Group | Cavicchi A.,University of Perugia | Postrioti L.,University of Perugia | Ungaro C.,Loccioni Group
SAE Technical Papers | Year: 2017

In the present paper, a new methodology for the estimation of the mass delivered by a single hole of a GDI injector is presented and discussed. The GDI injector used for the activity featured a five-hole nozzle characterized by three holes with the same diameter and two holes with a larger diameter. The different holes size guarantees a significant difference in terms of mass flow. This new methodology is based on global momentum flux measurement of each single plume and on its combination with the global mass measurement made with the gravimetric principle. The momentum flux is measured by means of a dedicated test bench that detects the impact force of the single spray plume at different distances. The sensing device is moved in different positions and, in each point, the force trace averaged over several injection events is acquired. The global mass delivered by the injector is measured by collecting and weighing the fuel flown during a defined number of consecutive injections. By the combination of these two measurements, the estimation of the single hole mass is proposed. The method is validated by means of a dedicated device that is able to collect the mass of the single hole. The method is applied in several operating conditions in terms of injection pressure and actuation time, obtaining encouraging results in terms of hole-to-hole injected mass evaluation capability. Copyright © 2017 SAE International.


Zimmermann H.,Neoplas Control GmbH | Wiese M.,Neoplas Control GmbH | Fiorani L.,Loccioni Group | Ragnoni A.,Loccioni Group
Journal of Sensors and Sensor Systems | Year: 2017

Because of their direct impact on patients, medical supply lines are under strict regulations and have to be monitored in terms of purity on a regular basis. State-of-the-art measurement solutions do not allow for continuous bedside monitoring. The aim of the presented project is to provide a compact multispecies monitoring system based on the latest quantum cascade laser technologies. © 2017 Author(s).


Rossetti D.,Marche Polytechnic University | Squartini S.,Marche Polytechnic University | Collura S.,Loccioni Group
Smart Innovation, Systems and Technologies | Year: 2016

This paper aims to evaluate the effectiveness of different Machine Learning algorithms for the estimation of Particle Size Distribution (PSD) of powder by means of Acoustic Emissions (AE). In industrial plants it is very useful to use noninvasive and adaptable systems for monitoring the particle size, for this reason the AE represents an important mean for detecting the particle size. To create a model that relates the AE with the powder size, Machine Learning is a viable approach to model a complex system without knowing all the variables in details. The test results show a good estimation accuracy for the various Machine Learning algorithms employed in this study. © Springer International Publishing Switzerland 2016.


Bastari A.,Loccioni Group | Cristalli C.,Loccioni Group | Morlacchi R.,Loccioni Group | Pomponi E.,Marche Polytechnic University
Mechanical Systems and Signal Processing | Year: 2011

The present work introduces an innovative method for measuring particle size distribution of an airborne powder, based on the application of signal processing techniques to the acoustic emission signals produced by the impacts of the powder with specific metallic surfaces. The basic idea of the proposed methodology lies on the identification of the unknown relation between the acquired acoustic emission signals and the powder particle size distribution, by means of a multi-step procedure. In the first step, wavelet packet decomposition is used to extract useful features from the acoustic emission signals; the dimensionality of feature space is further reduced through multivariate data analysis techniques. As a final step, a neural network is properly trained to map the feature vector into the particle size distribution. The proposed solution has several advantages, such as low cost and low invasiveness which allow the system based on this technique to be easily integrated in pre-existing plants. It has been successfully applied to the PSD measurement of coal powder produced by grinding mills in a coal-fired power station, and the experimental results are reported in the paper. The measurement principle can also be applied to different particle sizing applications, whenever a solid powder is carried in air or in other gases. © 2010 Elsevier Ltd.All rights reserved.


Grabowski D.,Silesian University of Technology | Cristalli C.,Loccioni Group
Infrared Physics and Technology | Year: 2015

This paper addresses the problem how to bring advanced data analysis techniques to the reality of a production line in order to increase the productivity and cost-effectiveness while reducing failure rates and increasing reliability of the final product. The main goal was to develop techniques of fast thermal inspection for production line quality control using a knowledge-based machine vision system. The paper contains a description of the system as well as a proposition of the algorithm for automatic classification of devices on the base of information included in their infrared images. Data-driven pattern recognition, infrared imaging, and principal component analysis (PCA) were put together and resulted in a very effective production line quality control system. The algorithm has been validated using real production line data. Experiments revealed some interesting features of the proposed method, e.g. resistance to changes of the ambient temperature and early classification during the thermal transient state. © 2015 Elsevier B.V. All rights reserved.


Comodi G.,Marche Polytechnic University | Giantomassi A.,Marche Polytechnic University | Severini M.,Marche Polytechnic University | Squartini S.,Marche Polytechnic University | And 5 more authors.
Applied Energy | Year: 2015

The paper presents the operational results of a real life residential microgrid which includes six apartments, a 20. kWp photovoltaic plant, a solar based thermal energy plant, a geothermal heat pump, a thermal energy storage, in the form of a 1300. l water tank and two 5.8. kW. h batteries supplying, each, a couple of apartments. Thanks to the thermal energy storage, the solar based thermal energy plant is able to satisfy the 100% of the hot water summer demand. Therefore the thermal energy storage represents a fundamental element in the management of the residential demand of thermal energy. It collects renewable thermal energy during day-time to release it during night-time, effectively shaving the peak of the thermal energy demand. The two electric storages, on the other hand, provide the hosted electrical subsystems with the ability to effectively increase the self-consumption of the local energy production, thus lowering the amount of energy surplus to be sold back to the grid, and increasing the self-sufficiency of the microgrid. For instance, the storage has supported self-consumption up to the 58.1% of local energy production with regard to the first battery, and up to the 63.5% with regard to the second one. Also, 3165 and 3365 yearly hours of fully autonomous activity have been recorded thanks to the first, and the second battery respectively. On the other hand, the yearly average efficiency amounts to 63.7%, and 65.3% respectively, for the first and second battery. In the second part of the paper we propose a computational framework to evaluate the overall performance of the microgrid system, while accounting different operating conditions and energy management policies. From this perspective, the framework acts as a useful modeling and design tool, to assess the opportunity of employing alternative energy management system topologies and strategies. Eight different configurations, with growing complexity, have been derived from the original system on purpose. The simulations, carried out based on real data related to one-year time period, have provided results showing that, the higher the integration level of electrical and thermal storage is, the higher degree of self-sufficiency can be achieved by the microgrid, and, in turn, the more consistent the yearly energy saving become. Nevertheless, despite the energy cost reduction achievable with the availability of storage systems in the Leaf House, their high investment cost made them not really profitable at the current price conditions for devices and energy purchase. © 2014 Elsevier Ltd.


Bastari A.,Loccioni Group | Bruni A.,Loccioni Group | Cristalli C.,Loccioni Group
IEEE International Symposium on Industrial Electronics | Year: 2010

An automated procedure for classification of poly-crystalline silicon solar cells with respect to their electrical characteristics is presented in this work. Electrical characteristics of solar cells are a very important issue in the photovoltaic panel production process, as they affect the final product quality. The procedure is composed of two sequential steps: in the first step a vector of features is extracted from the Electroluminescence intensity images of photovoltaic cells, making use of a texture analysis technique named Sum and Difference Histogram. In the second step the classification is carried out through a particular structure of Neural Network and a proper decision rule. The technique is especially suited to be implemented in production line, as it is fast and has a low computational complexity. Moreover, experimental results demonstrate the good performances in terms of successful classification. © 2010 IEEE.


Cellura M.,DREAM | Campanella L.,DREAM | Ciulla G.,DREAM | Guarino F.,University of Palermo | And 3 more authors.
ASHRAE Transactions | Year: 2011

In the framework of international actions to reduce the energy requirements and greenhouse gases emissions due to buildings, a new International Energy Agency task has been recently established in order to study Net Zero Energy Buildings (NZEBs). The commonly shared concept of NZEB, is a building whose annual balance of energy consumptions tends to zero. This concept is still too imprecise and the authors of this paper participate to the activities of SubTask B of IEA Task40 with the aim of establishing an internationally agreed understanding on NZEBs. The task is based on a common methodology for identifying and refining design approaches and tools to support industry adoption of innovative demand/supply technologies for NZEBS. This goal is pursued through detailed modeling and analysis of specific NZEB case studies. Among the specific objectives of the Sub Task B it is possible to include the analysis of redesigned studies. Redesigned studies should identify better alternative solutions for plants, building envelope or impact on the environment that significantly modify the building. To provide high quality information about the design process of a NZEB, it was decided to examine in detail the specific case study of the Leaf House (LH) located in Ancona, Italy. The studied building is fully monitored in terms of thermal environment, energy production and consumption, water use and occupancy. The purpose of this paper is to present some optionsto improve the performance of the selected building, identified by using the collected data and analyzing a detailed TRNSYS model of plant-building complex. The model has allowed detailed evaluation of the effects of some changes in the design that can improve the behavior of the Leaf House in terms of consumptions of energy resources and environmental impact of the building. The performed analysis shows that the building envelope is already very effective in terms of thermal performance, while the redesign of the thermal plants and the PV system should permit to reach a nearly net zero energy performance. © 2011 ASHRAE.

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