9 research outputs found

    Rivestimenti e gruppo fondamentale

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    Algoritmi Euristici per Problemi di Massimo Taglio

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    Condition Monitoring by Model-of-Signals: Application to gearbox lubrication

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    In this work, we make use of the Model-of-Signal technique to perform lubrication monitoring of a large industrial worm gear motor. We assume sensor measurements to be modelled by autoregressive processes and exploit the edge-computing capabilities of programmable logic controllers to perform the Recursive Least Squares algorithm to identify them. Then, we use those models to compute indicators able to diagnose the lubricant level within the gearbox and compare them to statistical indexes, which are traditionally used for monitoring. The aim of this application is to show how to build a condition monitoring infrastructure in an industrial environment able to detect possible occurring faults locally and acquire knowledge about them by exchanging information with external computers, paving the way towards Intelligent Maintenance Systems in Industry 4.0

    Condition Monitoring of a Paper Feeding Mechanism Using Model-of-Signals as Machine Learning Features

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    Prognostics and Health Management of machine devices and parts is a hot topic in the Industry 4.0 era. In this fashion, automated procedures to evaluate machinery working conditions are essential to minimize downtime and maintenance costs. In this work, we study how to monitor the decrease in performance of a paper sheet feeder for the packaging industry under heavy-duty cycle operations. The main measurable outcome of such degradation is the increase in backlash among the device moving components. A wide variety of methods and procedures is available to tackle this monitoring problem. In this paper, we analyze the use of a simple yet efficient diagnosis methodology that can exploit machinery controllers (i.e., Programmable Logic Controllers) edge-computing capabilities. Vibration measurements are known in the literature to retain information about the system's mechanics. Model-of Signals, a data-driven approach based on black box system identification, allows to extract that information reliably during machinery working cycle. The refinement of those data using machine learning allows the retrieval of knowledge about the health state of the machine. In this study, the feeder mechanism is run to failure with its parts backlash measured at given time intervals. Accelerometer signals are modelled as AutoRegressive processes whose coefficients are then considered as features to feed to machine learning algorithms, which are employed to perform severity evaluation of the ongoing degradation. Estimation and prediction are both implementable on-board the controller, while the learning task can be carried out remotely, in a cloud computing perspective. The exploitation of AutoRegressive modelling gives a simple and inherent methodology for feature selection, serving as a foundation of the machine learning stage. We make use of a Support Vector Machine algorithm to analyze how obtained models represent the various levels of backlash in the device and develop a suitable predictor of the degradation severity. Finally, the results of the application of the methodology to the case study are shown

    Primary Sarcoma of the Specialised Prostatic Stroma: A Case Report and Review of the Literature

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    Primary sarcoma tumours of the prostate are rare and are classified, according to their histology, as stromal tumours of uncertain malignant potential (STUMP) and stromal prostatic sarcoma (PS; low and high grade). We describe a case of a 71-year-old man that developed progressive urinary obstruction symptoms and was subjected to a transurethral prostatic resection (TURP). Histologically, there is a diffuse proliferation of epithelioid and spindle cells that showed rare atypical mitotic figures. Immunohistochemically, the neoplastic cells express diffusely CD34 and focally progesterone whereas no immunoreactivity was seen for cytocheratin, desmin, S-100, Bcl-2, chromogranin, CD117, and actin smooth muscle. A final diagnosis of low-grade prostatic stromal sarcoma (LG-PS) was made. This is a really rare neoplasm; in the literature, in fact, to our knowledge, only 6 cases are described and all of these were alive and free of disease at followup. Our patient too is free of disease at 15 months from the diagnosis

    First Materials on the Presence of Photography at the Scuola Grande di San Rocco in Venice

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    Charged Particle Multiplicities in pp Interactions Measured with the ATLAS Detector at the LHC

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    Measurements are presented from proton‚Äďproton collisions at centre-of-mass energies of \sqrt{s} = 0.9 , 2.36 and 7‚ÄČTeV recorded with the ATLAS detector at the LHC. Events were collected using a single-arm minimum-bias trigger. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity and the relationship between the mean transverse momentum and charged-particle multiplicity are measured. Measurements in different regions of phase space are shown, providing diffraction-reduced measurements as well as more inclusive ones. The observed distributions are corrected to well-defined phase-space regions, using model-independent corrections. The results are compared to each other and to various Monte Carlo (MC) models, including a new AMBT1 pythia6 tune. In all the kinematic regions considered, the particle multiplicities are higher than predicted by the MC models. The central charged-particle multiplicity per event and unit of pseudorapidity, for tracks with pT>100‚ÄČMeV, is measured to be 3.483¬Ī0.009 (stat)¬Ī0.106 (syst) at \sqrt{s} = 0.9\,{\rm TeV} and 5.630¬Ī0.003 (stat)¬Ī0.169 (syst) at \sqrt{s} = 7\,{\rm TeV}
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