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.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Space charge and dielectric response measurements to assess insulation aging of low-voltage cables used in nuclear power plants

The current design life of nuclear power plant (NPP) could potentially be extended to 80 years. During this extended plant life, all safety and operationally relevant Instrumentation & Control (I&C) systems are required to meet their designed performance requirements to ensure safe and reliable operation of the NPP, both during normal operation and subsequent to design base events. This in turn requires an adequate and documented qualification and aging management program. It is known that electrical insulation of I&C cables used in safety related circuits can degrade during their life, due to the aging effect of environmental stresses, such as temperature, radiation, vibration, etc., particularly if located in the containment area of the NPP. Thus several condition monitoring techniques are required to assess the state of the insulation. Such techniques can be used to establish a residual lifetime, based on the relationship between condition indicators and ageing stresses, hence, to support a preventive and effective maintenance program. The object of this thesis is to investigate potential electrical aging indicators (diagnostic markers) testing various I&C cable insulations subjected to an accelerated multi-stress (thermal and radiation) aging.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

Finite Element Modeling of Spiral Frequency Steerable Acoustic Transducers (FSATs) for guided waves based Structural Health Monitoring of plate-like structures

Structural Health Monitoring (SHM) is an emerging area of research associated to improvement of maintainability and the safety of aerospace, civil and mechanical infrastructures by means of monitoring and damage detection. Guided wave structural testing method is an approach for health monitoring of plate-like structures using smart material piezoelectric transducers. Among many kinds of transducers, the ones that have beam steering feature can perform more accurate surface interrogation. A frequency steerable acoustic transducer (FSATs) is capable of beam steering by varying the input frequency and consequently can detect and localize damage in structures. Guided wave inspection is typically performed through phased arrays which feature a large number of piezoelectric transducers, complexity and limitations. To overcome the weight penalty, the complex circuity and maintenance concern associated with wiring a large number of transducers, new FSATs are proposed that present inherent directional capabilities when generating and sensing elastic waves. The first generation of Spiral FSAT has two main limitations. First, waves are excited or sensed in one direction and in the opposite one (180 ̊ ambiguity) and second, just a relatively rude approximation of the desired directivity has been attained. Second generation of Spiral FSAT is proposed to overcome the first generation limitations. The importance of simulation tools becomes higher when a new idea is proposed and starts to be developed. The shaped transducer concept, especially the second generation of spiral FSAT is a novel idea in guided waves based of Structural Health Monitoring systems, hence finding a simulation tool is a necessity to develop various design aspects of this innovative transducer. In this work, the numerical simulation of the 1st and 2nd generations of Spiral FSAT has been conducted to prove the directional capability of excited guided waves through a plate-like structure.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Analisi e valutazione delle performance acustiche degli aeromobili nella fase di atterraggio

Nella tesi si analizzano le principali fonti del rumore aeronautico, lo stato dell'arte dal punto di vista normativo, tecnologico e procedurale. Si analizza lo stato dell'arte anche riguardo alla classificazione degli aeromobili, proponendo un nuovo indice prestazionale in alternativa a quello indicato dalla metodologia di certificazione (AC36-ICAO) Allo scopo di diminuire l'impatto acustico degli aeromobili in fase di atterraggio, si analizzano col programma INM i benefici di procedure CDA a 3° rispetto alle procedure tradizionali e, di seguito di procedure CDA ad angoli maggiori in termini di riduzione di lunghezza e di area delle isofoniche SEL85, SEL80 e SEL75.

Spatial and temporal variation in settlement, growth and condition of the rockfish species Sebastes caurinus and Sebastes carnatus in the Channel Islands region, Santa Barbara, CA

Survival during the early life stages of marine species, including nearshore temperate reef fishes, is typically very low, and small changes in mortality rates, due to physiological and environmental conditions, can have marked effects on survival of a cohort and, on a larger scale, on the success of a recruitment season. Moreover, trade offs between larval growth and accumulation of energetic resources prior to settlement are likely to influence growth and survival until this critical period and afterwards. Rockfish recruitment rates are notoriously variable between years and across geographic locations. Monitoring of rates of onshore delivery of pelagic juveniles (defined here as settlement) of two species of nearshore rockfishes, Sebastes caurinus and Sebastes carnatus, was done between 2003-2009 years using artificial collectors placed at San Miguel and Santa Cruz Island, off Southern California coast. I investigated spatiotemporal variation in settlement rate, lipid content, pelagic larval duration and larval growth of the newly settled fishes; I assessed relationships between birth date, larval growth, early life-history characteristics and lipid content at settlement, considering also interspecific differences; finally, I attempt to relate interannual patterns of settlement and of early life history traits to easily accessible, local and regional indices of ocean conditions including in situ ocean temperature and regional upwelling, sea surface temperature (SST) and Chlorophyll-a (Chl-a) concentration. Spatial variations appeared to be of low relevance, while significant interannual differences were detected in settlement rate, pelagic larval duration and larval growth. The amount of lipid content of the newly settled fishes was highly variable in space and time, but did not differ between the two species and did not show any relationships with early life history traits, indicating that no trade off involved these physiological processes or they were masked by high individual variability in different periods of larval life. Significant interspecific differences were found in the timing of parturition and settlement and in larval growth rates, with S. carnatus growing faster and breeding and settling later than S. caurinus. The two species exhibited also different patterns of correlations between larval growth rates and larval duration. S. carnatus larval duration was longer when the growth in the first two weeks post-hatch was faster, while S. caurinus had a shorter larval duration when grew fast in the middle and in the end of larval life, suggesting different larval strategies. Fishes with longer larval durations were longer in size at settlement and exhibited longer planktonic phase in periods of favourable environmental conditions. Ocean conditions had a low explanatory power for interannual variation in early life history traits, but a very high explanatory power for settlement fluctuations, with regional upwelling strength being the principal indicator. Nonetheless, interannual variability in larval duration and growth were related to great phenological changes in upwelling happened during the period of this study and that caused negative consequences at all trophic levels along the California coast. Despite the low explanatory power of the environmental variables used in this study on the variation of larval biological traits, environmental processes were differently related with early life history characteristics analyzed to species, indicating possible species-specific susceptibility to ocean conditions and local environmental adaptation, which should be further investigated. These results have implications for understanding the processes influencing larval and juvenile survival, and consequently recruitment variability, which may be dependent on biological characteristics and environmental conditions.