A probabilistic approach to analyse Blade Tip Timing data of non-synchronous vibrations under constant rotor speed

Blades are among the most critical components of turbomachines, their monitoring and characterization undergoing working conditions are fundamental for the insiders, both for preventing eventual breakage and for optimising future development. Two approaches are possible for monitoring rotor blade vibrations: a traditional one based on the use of strain gauges and another one called Blade Tip Timing (BTT). BTT is an indirect, non-intrusive simple and robust measurement method, but the processing of such data is not easy because they are often subsampled with respect to the Nyquist limit and the ordering of the samples is not unique. In this work the focus is on multi component non-synchronous vibrations, typical for example of flutter, measured at constant rotor speed by a BTT system. These data are organized into batches of fixed length called snapshots and they are interpreted as members of a random vector. When the signal contains only one harmonic component the frequency can be determined using a method here described and called Harmonic Matching (HM). While for the analyses of multi harmonic component vibrations a probabilistic approach capable of separating and identify the components using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed. For the development of data processing methods, the possibility of having controllable and repeatable data is fundamental, for this reason two test rigs of increasing complexity have been developed and are here described

Hepatitis D: Thirty years after

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Visual Anomaly Detection on Circular Plastic Parts Using Generative Adversarial Networks

openIn the recent years, automated quality control systems have been estabilished as the main method for anomaly detection, term which refers to the process of identifying and flagging any abnormality in the condition of the given components. Given their efficiency, many new methods were developed, mainly exploiting Computer Vision algorithms, but they have their limitations. In a similar way, many studies were applied on Neural Networks and Machine Learning algorithms, with the development of Convolutional Neural Networks, Transformers and Generative Adversarial Networks (GANs). The objective of this thesis is to develop an automated quality control system exploiting the generative and adversarial qualities of the current state-of-the-art methods based on Neural Networks. The main tool used for this task is the capability of the GANs to learn how a flawless input should look, so that the pipeline can identify inputs with anomalies. The developed solution was tested on a real world problem, aiming to indentify cracks and anomalies in plastic motor covers.In the recent years, automated quality control systems have been estabilished as the main method for anomaly detection, term which refers to the process of identifying and flagging any abnormality in the condition of the given components. Given their efficiency, many new methods were developed, mainly exploiting Computer Vision algorithms, but they have their limitations. In a similar way, many studies were applied on Neural Networks and Machine Learning algorithms, with the development of Convolutional Neural Networks, Transformers and Generative Adversarial Networks (GANs). The objective of this thesis is to develop an automated quality control system exploiting the generative and adversarial qualities of the current state-of-the-art methods based on Neural Networks. The main tool used for this task is the capability of the GANs to learn how a flawless input should look, so that the pipeline can identify inputs with anomalies. The developed solution was tested on a real world problem, aiming to indentify cracks and anomalies in plastic motor covers

Analytical methods and simulation models to assess innovative operational measures and technologies for rail port terminals: the case of Valencia Principe Felipe terminal

The topic of freight transport by rail is a complex theme and, in recent years, a main issue of European policy. The legislation evolution and the White Paper 2011 have demonstrated the European intention to re-launch this sector. The challenge is to promote the intermodal transport system to the detriment of road freight transport. In this context intermodal freight terminals, play a primary role for the supply chain, they are the connection point between the various transport nodes and the nodal points where the freight are handled, stored and transferred between different modes to final customer. To achieve the purpose, it is strengthen the improvement of existing intermodal freight terminals and the development of innovative intermodal freight terminals towards higher performance (ERRAC, 2012). Many terminal performances improvements have been proposed and sometime experimented. They are normally basing on combinations of operational measures and innovative technologies (e.g. automatic horizontal and parallel storage and handling, automated gate and sensors for tracking systems data exchange) tested in various terminals, with often-contradictory results. The research work described in this paper (developed within the Capacity4Rail EU project) focusses on the assessment of effects that these innovations can have in the intermodal freight terminals combined in various alternative consistent effective scenarios. The methodological framework setup to assess these innovations is basing on a combination of analytical methods based on sequential algorithms and discrete events simulation models. The output of this assessment method are key performance indicators (KPIs) selected according to terminals typologies and related to different aspects (e.g. management, operation and organization). The present paper illustrates the application of the methodological framework, tuned on the operation of various intermodal terminals, for the validation on today operation and the assessment of possible future scenarios to the case study of the Principe Felipe sea-rail terminal in Valencia

Experimental Investigation on a Bladed Disk with Traveling Wave Excitation

Bladed disks are key components of turbomachines and their dynamic behavior is strongly conditioned by their small accidental lack of symmetry referred to as blade mistuning. The experimental identification of mistuned disks is complicated due to several reasons related both to measurement and data processing issues. This paper describes the realization of a test rig designed to investigate the behavior of mistuned disks and develop or validate data processing techniques for system identification. To simplify experiments, using the opposite than in the real situation, the disk is fixed, while the excitation is rotating. The response measured during an experiment carried out in the resonance-crossing condition is used to compare three alternative techniques to estimate the frequency-response function of the disk