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.

Qualitatitive and quantitative analysis of fish spoilage processes for development of new food monitoring systems

This study investigates the growth and metabolite production of microorganisms causing spoilage of Atlantic cod (Gadus morhua) fillets packaged under air and modified atmosphere (60 % CO2, 40 % O2). Samples were provided by two different retailers (A and B). Storage of packaged fillets occurred at 4 °C and 8 °C. Microbiological quality and metabolite production of cod fillets stored in MAP 4 °C, MAP 8 °C and air were monitored during 13 days, 7 days and 3 days of storage, respectively. Volatile compounds concentration in the headspace were quantified by Selective ion flow tube mass spectrometry and a correlation with microbiological spoilage was studied. The onset of volatile compounds detection was observed to be mostly around 7 log cfu/g of total psychrotrophic count. Trimethylamine and dimethyl sulfide were found to be the dominant volatiles in all of the tested storage conditions, nevertheless there was no close correlation between concentrations of each main VOC and percentages of rejection based on sensory evaluation. According to results it was concluded that they cannot be considered as only indicators of the quality of cod fillets stored in modified atmosphere and air.  

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.

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.

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.

A new method for continuous quality control of nsm cfrp systems

In the last decade the near-surface mounted (NSM) strengthening technique using carbon fibre reinforced polymers (CFRP) has been increasingly used to improve the load carrying capacity of concrete members. Compared to externally bonded reinforcement (EBR), the NSM system presents considerable advantages. This technique consists in the insertion of carbon fibre reinforced polymer laminate strips into pre-cut slits opened in the concrete cover of the elements to be strengthened. CFRP reinforcement is bonded to concrete with an appropriate groove filler, typically epoxy adhesive or cement grout. Up to now, research efforts have been mainly focused on several structural aspects, such as: bond behaviour, flexural and/or shear strengthening effectiveness, and energy dissipation capacity of beam-column joints. In such research works, as well as in field applications, the most widespread adhesives that are used to bond reinforcements to concrete are epoxy resins. It is largely accepted that the performance of the whole application of NSM systems strongly depends on the mechanical properties of the epoxy resins, for which proper curing conditions must be assured. Therefore, the existence of non-destructive methods that allow monitoring the curing process of epoxy resins in the NSM CFRP system is desirable, in view of obtaining continuous information that can provide indication in regard to the effectiveness of curing and the expectable bond behaviour of CFRP/adhesive/concrete systems. The experimental research was developed at the Laboratory of the Structural Division of the Civil Engineering Department of the University of Minho in Guimar\~aes, Portugal (LEST). The main objective was to develop and propose a new method for continuous quality control of the curing of epoxy resins applied in NSM CFRP strengthening systems. This objective is pursued through the adaptation of an existing technique, termed EMM-ARM (Elasticity Modulus Monitoring through Ambient Response Method) that has been developed for monitoring the early stiffness evolution of cement-based materials. The experimental program was composed of two parts: (i) direct pull-out tests on concrete specimens strengthened with NSM CFRP laminate strips were conducted to assess the evolution of bond behaviour between CFRP and concrete since early ages; and, (ii) EMM-ARM tests were carried out for monitoring the progressive stiffness development of the structural adhesive used in CFRP applications. In order to verify the capability of the proposed method for evaluating the elastic modulus of the epoxy, static E-Modulus was determined through tension tests. The results of the two series of tests were then combined and compared to evaluate the possibility of implementation of a new method for the continuous monitoring and quality control of NSM CFRP applications.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.

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.

Real-time PMU monitoring of an IEEE 5-Bus Network implemented on OPAL system

This thesis studies the state-of-the-art of phasor measurement units (PMUs) as well as their metrological requirements stated in the IEEE C37.118.1 and C37.118.2 Standards for guaranteeing correct measurement performances. Communication systems among PMUs and their possible applicability in the field of power quality (PQ) assessment are also investigated. This preliminary study is followed by an analysis of the working principle of real-time (RT) simulators and the importance of hardware-in-the-loop (HIL) implementation, examining the possible case studies specific for PMUs, including compliance tests which are one of the most important parts. The core of the thesis is focused on the implementation of a PMU model in the IEEE 5-bus network in Simulink and in the validation of the results using OPAL RT-4510 as a real-time simulator. An initial check allows one to get an idea about the goodness of the results in Simulink, comparing the PMU data with respect to the load-flow steady-state information. In this part, accuracy indices are also calculated for both voltage and current synchrophasors. The following part consists in the implementation of the same code in OPAL-RT 4510 simulator, after which an initial analysis is carried out in a qualitative way in order to get a sense of the goodness of the outcomes. Finally, the confirmation of the results is based on an examination of the attained voltage and current synchrophasors and accuracy indices coming from Simulink models and from OPAL system, using a Matlab script. This work also proposes suggestions for an upcoming operation of PMUs in a more complex system as the Digital Twin (DT) in order to improve the performances of the already-existing protection devices of the distribution system operator (DSO) for a future enhancement of power systems reliability.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Industrial Demilitarized Zone and Zero Trust cybersecurity models for Industrial Control Systems

Today more than ever, with the recent war in Ukraine and the increasing number of attacks that affect systems of nations and companies every day, the world realizes that cybersecurity can no longer be considered just as a “cost”. It must become a pillar for our infrastructures that involve the security of our nations and the safety of people. Critical infrastructure, like energy, financial services, and healthcare, have become targets of many cyberattacks from several criminal groups, with an increasing number of resources and competencies, putting at risk the security and safety of companies and entire nations. This thesis aims to investigate the state-of-the-art regarding the best practice for securing Industrial control systems. We study the differences between two security frameworks. The first is Industrial Demilitarized Zone (I-DMZ), a perimeter-based security solution. The second one is the Zero Trust Architecture (ZTA) which removes the concept of perimeter to offer an entirely new approach to cybersecurity based on the slogan ‘Never Trust, always verify’. Starting from this premise, the Zero Trust model embeds strict Authentication, Authorization, and monitoring controls for any access to any resource. We have defined two architectures according to the State-of-the-art and the cybersecurity experts’ guidelines to compare I-DMZ, and Zero Trust approaches to ICS security. The goal is to demonstrate how a Zero Trust approach dramatically reduces the possibility of an attacker penetrating the network or moving laterally to compromise the entire infrastructure. A third architecture has been defined based on Cloud and fog/edge computing technology. It shows how Cloud solutions can improve the security and reliability of infrastructure and production processes that can benefit from a range of new functionalities, that the Cloud could offer as-a-Service.We have implemented and tested our Zero Trust solution and its ability to block intrusion or attempted attacks.

Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard’s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.