Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Applicazioni tecnologiche e nuovi metodi di valutazione della performance negli sport acquatici

Nello sport di alto livello l’uso della tecnologia ha raggiunto un ruolo di notevole importanza per l’analisi e la valutazione della prestazione. Negli ultimi anni sono emerse nuove tecnologie e sono migliorate quelle pre-esistenti (i.e. accelerometri, giroscopi e software per l’analisi video) in termini di campionamento, acquisizione dati, dimensione dei sensori che ha permesso la loro “indossabilità” e l’inserimento degli stessi all’interno degli attrezzi sportivi. La tecnologia è sempre stata al servizio degli atleti come strumento di supporto per raggiungere l’apice dei risultati sportivi. Per questo motivo la valutazione funzionale dell’atleta associata all’uso di tecnologie si pone lo scopo di valutare i miglioramenti degli atleti misurando la condizione fisica e/o la competenza tecnica di una determinata disciplina sportiva. L’obiettivo di questa tesi è studiare l’utilizzo delle applicazioni tecnologiche e individuare nuovi metodi di valutazione della performance in alcuni sport acquatici. La prima parte (capitoli 1-5), si concentra sulla tecnologia prototipale chiamata E-kayak e le varie applicazioni nel kayak di velocità. In questi lavori è stata verificata l’attendibilità dei dati forniti dal sistema E-kayak con i sistemi presenti in letteratura. Inoltre, sono stati indagati nuovi parametri utili a comprendere il modello di prestazione del paddler. La seconda parte (capitolo 6), si riferisce all’analisi cinematica della spinta verticale del pallanuotista, attraverso l’utilizzo della video analisi 2D, per l’individuazione delle relazioni Forza-velocità e Potenza-velocità direttamente in acqua. Questo studio pilota, potrà fornire indicazioni utili al monitoraggio e condizionamento di forza e potenza da svolgere direttamente in acqua. Infine la terza parte (capitoli 7-8), si focalizza sull’individuazione della sequenza di Fibonacci (sequenza divina) nel nuoto a stile libero e a farfalla. I risultati di questi studi suggeriscono che il ritmo di nuotata tenuto durante le medie/lunghe distanze gioca un ruolo chiave. Inoltre, il livello di autosomiglianza (self-similarity) aumenta con la tecnica del nuoto.

Test vector generation for the phase II ATLAS event filter trigger upgrade

In the near future, the LHC experiments will continue to be upgraded as the LHC luminosity will increase from the design 1034 to 7.5 × 1034, with the HL-LHC project, to reach 3000 × f b−1 of accumulated statistics. After the end of a period of data collection, CERN will face a long shutdown to improve overall performance by upgrading the experiments and implementing more advanced technologies and infrastructures. In particular, ATLAS will upgrade parts of the detector, the trigger, and the data acquisition system. It will also implement new strategies and algorithms for processing and transferring the data to the final storage. This PhD thesis presents a study of a new pattern recognition algorithm to be used in the trigger system, which is a software designed to provide the information necessary to select physical events from background data. The idea is to use the well-known Hough Transform mathematical formula as an algorithm for detecting particle trajectories. The effectiveness of the algorithm has already been validated in the past, independently of particle physics applications, to detect generic shapes in images. Here, a software emulation tool is proposed for the hardware implementation of the Hough Transform, to reconstruct the tracks in the ATLAS Trigger and Data Acquisition system. Until now, it has never been implemented on electronics in particle physics experiments, and as a hardware implementation it would provide overall latency benefits. A comparison between the simulated data and the physical system was performed on a Xilinx UltraScale+ FPGA device.

Optimized methods to digital survey and 3D modelling of cultural heritage assets

Currently making digital 3D models and replicas of the cultural heritage assets play an important role in the preservation and having a high detail source for future research and intervention. In this dissertation, it is tried to assess different methods for digital surveying and making 3D replicas of cultural heritage assets in different scales of size. The methodologies vary in devices, software, workflow, and the amount of skill that is required. The three phases of the 3D modelling process are data acquisition, modelling, and model presentation. Each of these sections is divided into sub-sections and there are several approaches, methods, devices, and software that may be employed, furthermore, the selection process should be based on the operation's goal, available facilities, the scale and properties of the object or structure to be modeled, as well as the operators' expertise and experience. The most key point to remember is that the 3D modelling operation should be properly accurate, precise, and reliable; therefore, there are so many instructions and pieces of advice on how to perform 3D modelling effectively. It is an attempt to compare and evaluate the various ways of each phase in order to explain and demonstrate their differences, benefits, and drawbacks in order to serve as a simple guide for new and/or inexperienced users.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Calibration of the detector flight models for the HERMES and SPIRIT nanosatellite missions

The High Energy Rapid Modular Ensemble of Satellites (HERMES) is a new mission concept involving the development of a constellation of six CubeSats in low Earth orbit with new miniaturized instruments that host a hybrid Silicon Drift Detector/GAGG:Ce based system for X-ray and γ-ray detection, aiming to monitor high-energy cosmic transients, such as Gamma Ray Bursts and the electromagnetic counterparts of gravitational wave events. The HERMES constellation will also operate together with the Australian-Italian SpIRIT mission, which will house a HERMES-like detector. The HERMES pathfinder mini-constellation, consisting of six satellites plus SpIRIT, is likely to be launched in 2023. The HERMES detectors are based on the heritage of the Italian ReDSoX collaboration, with joint design and production by INFN-Trieste and Fondazione Bruno Kessler, and the involvement of several Italian research institutes and universities. An application-specific, low-noise, low-power integrated circuit (ASIC) called LYRA was conceived and designed for the HERMES readout electronics. My thesis project focuses on the ground calibrations of the first HERMES and SpIRIT flight detectors, with a performance assessment and characterization of the detectors. The first part of this work addresses measurements and experimental tests on laboratory prototypes of the HERMES detectors and their front-end electronics, while the second part is based on the design of the experimental setup for flight detector calibrations and related functional tests for data acquisition, as well as the development of the calibration software. In more detail, the calibration parameters (such as the gain of each detector channel) are determined using measurements with radioactive sources, performed at different operating temperatures between -20°C and +20°C by placing the detector in a suitable climate chamber. The final part of the thesis involves the analysis of the calibration data and a discussion of the results.

Algoritmo di tagging di raggi cosmici in un sistema di acquisizione triggerless per un esperimento di fisica di particelle su fascio

I raggi cosmici sono una fonte naturale di particelle ad alta energia di origine galattica e extragalattica. I prodotti della loro interazione con l’atmosfera terrestre giungono fino alla superficie terrestre, dove vengono rilevati dagli esperimenti di fisica delle particelle. Si vuole quindi identificare e rimuovere questo segnale. Gli apparati sperimentali usati in fisica delle particelle prevedono dei sistemi di selezione dei segnali in ingresso (detti trigger) per rigettare segnali sotto una certa soglia di energia. Il progredire delle prestazioni dei calcolatori permette oggi di sostituire l’elettronica dei sistemi di trigger con implementazioni software (triggerless) in grado di selezionare i dati secondo criteri più complessi. TriDAS (Triggerless Data Acquisition System) è un sistema di acquisizione triggerless sviluppato per l’esperimento KM3NeT e utilizzato recentemente per gestire l’acquisizione di esperimenti di collisione di fascio ai Jefferson Lab (Newport News, VA). Il presente lavoro ha come scopo la definizione di un algoritmo di selezione di eventi generati da raggi cosmici e la sua implementazione come trigger software all’interno di TriDAS. Quindi si mostrano alcuni strumenti software sviluppati per costruire un ambiente di test del suddetto algoritmo e analizzare i dati prodotti da TriDAS.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.

Design and implementation of a vibrational condition monitoring system

Industrial companies, particularly those with induction motors and gearboxes as integral components of their systems, are utilizing Condition Monitoring (CM) systems more frequently in order to discover the need for maintenance in advance, as traditional maintenance only performs tasks when a failure has been identified. Utilizing a CM system is essential to boost productivity and minimize long-term failures that result in financial loss. The more exact and practical the CM system, the better the data analysis, which adds to a more precise maintenance forecast. This thesis project is a cooperation with PEI Vibration Monitoring s.r.l. to design and construct a low-cost vibrational condition monitoring system to check the health of induction motors and gearboxes automatically. Moreover, according to the company's request, such a system should have specs comparable to NI 9234, one of the company's standard Data Acquisition (DAQ) boards, but at a significantly cheaper price. Additionally, PEI VM Company has supplied all hardware and electronic components. The suggested CM system is capable of highprecision autonomous monitoring of induction motors and gearboxes, and it consists of a Raspberry Pi 3B and MCC 172 DAQ board.

Acquisition and data analysis related to changes in some forest soils properties after prescribed fire action

Controlled fires in forest areas are frequently used in most Mediterranean countries as a preventive technique to avoid severe wildfires in summer season. In Portugal, this forest management method of fuel mass availability is also used and has shown to be beneficial as annual statistical reports confirm that the decrease of wildfires occurrence have a direct relationship with the controlled fire practice. However prescribed fire can have serious side effects in some forest soil properties. This work shows the changes that occurred in some forest soils properties after a prescribed fire action. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, that had not been burn for four years. The composed soil samples were collected from five plots at three different layers (0-3cm, 3-6cm and 6-18cm) during a three-year monitoring period after the prescribed burning. Principal Component Analysis was used to reach the presented conclusions.

The adjective petit 'small, little' in French acquisition data: an example of the relationship between pragmatics and morphosyntactic development

In French the adjective petit 'small, little' has a special status: it fulfills various pragmatic functions in addition to semantic meanings and it is thus highly frequent in discourse. Résumé: This study, based on the data of two children, aged 1;6 to 2;11, argues that petit and its pragmatic meanings play a specific role in the acquisition of French adjectives. In contrast to what is expected in child language, petit favours the early development of a pattern of noun phrase with prenominal attributive adjective. The emergence and distribution of petit in the children's production is examined and related to its distribution in the input, and the detailed pragmatic meanings and functions of petit are analysed. Prenominal petit emerges early as the preferred and most productive adjective. Pragmatic meanings of petit appear to be predominant in this early age and are of two main types: expressions of endearment (in noun phrases) and mitigating devices whose scope is the entire utterance. These results, as well as instances of children's pragmatic overgeneralizations, provide new evidence that at least some pragmatic meanings are prior to semantic meanings in early acquisition.

A cost-effective high-throughput digital system for observation and acquisition of animal behavioral data

We have designed and implemented a low-cost digital system using closed-circuit television cameras coupled to a digital acquisition system for the recording of in vivo behavioral data in rodents and for allowing observation and recording of more than 10 animals simultaneously at a reduced cost, as compared with commercially available solutions. This system has been validated using two experimental rodent models: one involving chemically induced seizures and one assessing appetite and feeding. We present observational results showing comparable or improved levels of accuracy and observer consistency between this new system and traditional methods in these experimental models, discuss advantages of the presented system over conventional analog systems and commercially available digital systems, and propose possible extensions to the system and applications to nonrodent studies.

A cost-effective high-throughput digital system for observation and acquisition of animal behavioral data

We have designed and implemented a low-cost digital system using closed-circuit television cameras coupled to a digital acquisition system for the recording of in vivo behavioral data in rodents and for allowing observation and recording of more than 10 animals simultaneously at a reduced cost, as compared with commercially available solutions. This system has been validated using two experimental rodent models: one involving chemically induced seizures and one assessing appetite and feeding. We present observational results showing comparable or improved levels of accuracy and observer consistency between this new system and traditional methods in these experimental models, discuss advantages of the presented system over conventional analog systems and commercially available digital systems, and propose possible extensions to the system and applications to non-rodent studies.

Methodology proposal for acquisition of biotelemetric data in a real time

This work presents a methodological proposal for acquisition of biometric data through telemetry basing its development on a research-action and a case study. Nowadays, the qualified professionals of physical evaluation have to use specific devices to obtain biometric signals and data. These devices in the most of the time are high cost and difficult to use and handling. Therefore, the methodological proposal was elaborate in order to develop, conceptually, a bio telemetric device which could acquire the desirable biometric signals: oxymetry, biometrics, corporal temperature and pedometry which are essential for the area of physical evaluation. It was researched the existent biometrics sensors, the possible ways for the remote transmission of signals and the computer systems available so that the acquisition of data could be possible. This methodological proposal of remote acquisition of biometrical signals is structured in four modules: Acquisitor of biometrics data; Converser and transmitter of biometric signals; Receiver and Processor of biometrics signals and Generator of Interpretative Graphs. The modules aim the obtention of interpretative graphics of human biometric signals. In order to validate this proposal a functional prototype was developed and it is presented in the development of this work.