Volunteer-based coral reef monitoring: reliability of data, environmental education and implications for conservation

Coral reefs are the most biodiverse ecosystems of the ocean and they provide notable ecosystem services. Nowadays, they are facing a number of local anthropogenic threats and environmental change is threatening their survivorship on a global scale. Large-scale monitoring is necessary to understand environmental changes and to perform useful conservation measurements. Governmental agencies are often underfunded and are not able of sustain the necessary spatial and temporal large-scale monitoring. To overcome the economic constrains, in some cases scientists can engage volunteers in environmental monitoring. Citizen Science enables the collection and analysis of scientific data at larger spatial and temporal scales than otherwise possible, addressing issues that are otherwise logistically or financially unfeasible. “STE: Scuba Tourism for the Environment” was a volunteer-based Red Sea coral reef biodiversity monitoring program. SCUBA divers and snorkelers were involved in the collection of data for 72 taxa, by completing survey questionnaires after their dives. In my thesis, I evaluated the reliability of the data collected by volunteers, comparing their questionnaires with those completed by professional scientists. Validation trials showed a sufficient level of reliability, indicating that non-specialists performed similarly to conservation volunteer divers on accurate transects. Using the data collected by volunteers, I developed a biodiversity index that revealed spatial trends across surveyed areas. The project results provided important feedbacks to the local authorities on the current health status of Red Sea coral reefs and on the effectiveness of the environmental management. I also analysed the spatial and temporal distribution of each surveyed taxa, identifying abundance trends related with anthropogenic impacts. Finally, I evaluated the effectiveness of the project to increase the environmental education of volunteers and showed that the participation in STEproject significantly increased both the knowledge on coral reef biology and ecology and the awareness of human behavioural impacts on the environment.

Ingegneria di Manutenzione

Lo scopo di questa tesi di dottorato di ricerca consiste nel fornire la giusta collocazione della manutenzione fra le discipline dell'ingegneria, raccogliendo e formalizzando le metodologie di analisi di affidabilità e di pianificazione degli interventi all'interno di un unico processo di progettazione e di controllo. In linea di principio, un processo di analisi dei guasti e di programmazione della manutenzione deve essere in grado di fornire chiare e sicure risposte ai seguenti interrogativi: Quali sono le funzioni richieste e con quali criteri di prestazioni il sistema è chiamato ad assolverle? Qual'è l'andamento della disponibilità del sistema in funzione del tempo? Quanti guasti e di quale tipo si possono verificare durante la vita del sistema? Quali possono essere le conseguenze che ledono la sicurezza e la protezione ambientale? Quanti pezzi di ricambio sono necessari? Che tipo di interventi di manutenzione preventiva risultano tecnicamente fattibili? A quali scadenze devono essere programmati? A quanto ammonta la previsione del costo di esercizio del sistema? Quante squadre di manutenzione devono essere assegnate al sistema? Come deve essere organizzata la logistica di manutenzione? Con quali tecniche si prevede di riconoscere i guasti e quali procedure devono essere attivate per farvi fronte? E' possibile implementare tecniche di `condition monitoring' delle macchine? Su quali tempi di preavviso sui guasti si può contare? In tal senso, la manutenzione necessita delle tecniche e degli opportuni strumenti che siano in grado di misurarne l'efficacia e l'efficienza. L'efficacia in primo luogo, in quanto l'obiettivo principe consiste nel garantire che il sistema oggetto di studio continui a svolgere le proprie funzioni nei limiti di prestazioni accettabili, secondo le specifiche richieste degli utilizzatori. L'efficienza in secondo luogo, ma non per questo di minore importanza, in quanto perseguendo l'obiettivo di cui sopra, occorre impegnare il minimo di risorse possibili, organizzando con razionalità il supporto logistico del sistema al fine di raggiungere i massimi livelli di rendimento di gestione. La migliore strategia di manutenzione può essere pianificata, a priori, solo se si è in grado di prevedere con la necessaria precisione l'evoluzione del sistema nel suo contesto operativo futuro. E' allora possibile formulare un modello matematico del sistema, studiarne la dinamica ed osservare le reazioni alla simulazione di eventuali stimoli esterni. I metodi ed i modelli noti dell'ingegneria dei sistemi possono essere molto utili per la risoluzione di casi semplici, ma sovente richiedono la formulazione di ipotesi troppo restrittive che aumentano in modo inaccettabile la distanza del modello dalla realtà. Una strada alternativa ed affascinante, che ho percorso con entusiasmo durante questi tre anni di studi e ricerca, consiste nella simulazione numerica della vita del sistema, utilizzando il metodo Monte Carlo per la gestione dei processi stocastici di guasto e per l'esecuzione degli interventi di manutenzione. Ho quindi messo a punto il codice di simulazione RAMSES, perseguendo l'idea di costruire uno strumento di misura dell'efficacia e dell'efficienza di una politica di manutenzione simulata al calcolatore. Nella tesi si presentano i concetti di base dell'ingegneria dei sistemi applicata al caso della manutenzione e si introduce il formalismo della Reliability Centred Maintenance come miglior guida nella pianificazione delle schede di manutenzione. Si introducono le nozioni di base per fornire una struttura solida e corretta alla simulazione numerica dei sistemi riparabili e si presenta il codice RAMSES corredando le informazioni tecniche con i dovuti esempi ed applicazioni pratiche. Si conclude il lavoro, infine, con la presentazione di un modello di massima verosimiglianza particolarmente utile per le analisi dei dati sperimentali di guasto dei componenti.

Aging of nuclear power plant cables: in search of non-destructive diagnostic quantities

The safety systems of nuclear power plants rely on low-voltage power, instrumentation and control cables. Inside the containment area, cables operate in harsh environments, characterized by relatively high temperature and gamma-irradiation. As these cables are related to fundamental safety systems, they must be able to withstand unexpected accident conditions and, therefore, their condition assessment is of utmost importance as plants age and lifetime extensions are required. Nowadays, the integrity and functionality of these cables are monitored mainly through destructive test which requires specific laboratory. The investigation of electrical aging markers which can provide information about the state of the cable by non-destructive testing methods would improve significantly the present diagnostic techniques. This work has been made within the framework of the ADVANCE (Aging Diagnostic and Prognostics of Low-Voltage I\&C Cables) project, a FP7 European program. This Ph.D. thesis aims at studying the impact of aging on cable electrical parameters, in order to understand the evolution of the electrical properties associated with cable degradation. The identification of suitable aging markers requires the comparison of the electrical property variation with the physical/chemical degradation mechanisms of polymers for different insulating materials and compositions. The feasibility of non-destructive electrical condition monitoring techniques as potential substitutes for destructive methods will be finally discussed studying the correlation between electrical and mechanical properties. In this work, the electrical properties of cable insulators are monitored and characterized mainly by dielectric spectroscopy, polarization/depolarization current analysis and space charge distribution. Among these techniques, dielectric spectroscopy showed the most promising results; by means of dielectric spectroscopy it is possible to identify the frequency range where the properties are more sensitive to aging. In particular, the imaginary part of permittivity at high frequency, which is related to oxidation, has been identified as the most suitable aging marker based on electrical quantities.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Strength and ductility of corroded strands within the framework of safety assessment of existing bridges

Existing bridges built in the last 50 years face challenges due to states far different than those envisaged when they were designed, due to increased loads, ageing of materials, and poor maintenance. For post-tensioned bridges, the need emerged for reliable engineering tools for the evaluation of their capacity in case of steel corrosion due to lack of mortar injection. This can lead to sudden brittle collapses, highlighting the need for proper maintenance and monitoring. This thesis proposes a peak strength model for corroded strands, introducing a “group coefficient” that aims at considering corrosion variability in the wires constituting the strands. The application of the introduced model in a deterministic approach leads to the proposal of strength curves for corroded strands, which represent useful engineering tools for estimating their maximum strength considering both geometry of the corrosion and steel material parameters. Together with the proposed ultimate displacement curves, constitutive laws of the steel material reduced by the effects of corrosion can be obtained. The effects of corroded strands on post-tensioned beams can be evaluated through the reduced bending moment-curvature diagram accounting for these reduced stress-strain relationships. The application of the introduced model in a probabilistic approach allows to estimate peak strength probability functions and consecutive design-oriented safety factors to consider corrosion effects in safety assessment verifications. Both approaches consider two procedures that are based on the knowledge level of the corrosion in the strands. On the sidelines of this main research line, this thesis also presents a study of a seismic upgrading intervention of a case-study bridge through HDRB isolators providing a simplified procedure for the identification of the correct device. The study also investigates the effects due to the variability of the shear modulus of the rubber material of the HDRB isolators on the structural response of the isolated bridge.

Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework

Over the last 60 years, computers and software have favoured incredible advancements in every field. Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property. This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections. The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components: 1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC), 2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning, 3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system. The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP). By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature. Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.

Development of motion analysis protocols based on inertial sensors

The aim of this thesis was to describe the development of motion analysis protocols for applications on upper and lower limb extremities, by using inertial sensors-based systems. Inertial sensors-based systems are relatively recent. Knowledge and development of methods and algorithms for the use of such systems for clinical purposes is therefore limited if compared with stereophotogrammetry. However, their advantages in terms of low cost, portability, small size, are a valid reason to follow this direction. When developing motion analysis protocols based on inertial sensors, attention must be given to several aspects, like the accuracy of inertial sensors-based systems and their reliability. The need to develop specific algorithms/methods and software for using these systems for specific applications, is as much important as the development of motion analysis protocols based on them. For this reason, the goal of the 3-years research project described in this thesis was achieved first of all trying to correctly design the protocols based on inertial sensors, in terms of exploring and developing which features were suitable for the specific application of the protocols. The use of optoelectronic systems was necessary because they provided a gold standard and accurate measurement, which was used as a reference for the validation of the protocols based on inertial sensors. The protocols described in this thesis can be particularly helpful for rehabilitation centers in which the high cost of instrumentation or the limited working areas do not allow the use of stereophotogrammetry. Moreover, many applications requiring upper and lower limb motion analysis to be performed outside the laboratories will benefit from these protocols, for example performing gait analysis along the corridors. Out of the buildings, the condition of steady-state walking or the behavior of the prosthetic devices when encountering slopes or obstacles during walking can also be assessed. The application of inertial sensors on lower limb amputees presents conditions which are challenging for magnetometer-based systems, due to ferromagnetic material commonly adopted for the construction of idraulic components or motors. INAIL Prostheses Centre stimulated and, together with Xsens Technologies B.V. supported the development of additional methods for improving the accuracy of MTx in measuring the 3D kinematics for lower limb prostheses, with the results provided in this thesis. In the author’s opinion, this thesis and the motion analysis protocols based on inertial sensors here described, are a demonstration of how a strict collaboration between the industry, the clinical centers, the research laboratories, can improve the knowledge, exchange know-how, with the common goal to develop new application-oriented systems.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

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.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.