Blockchain-based Digital Twins:Research Trends, Issues, and Future Challenges

Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross validated through multiple sensor sources with overlapping fields of view. Cross-validated data can then be stored on the blockchain, to maintain its integrity and trustworthiness. Once trustworthy data is recorded on the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, digital twins (DTs) can be leveraged to draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Empowering DTs with blockchain in industrial use cases targets key challenges of disparate data repositories, untrustworthy data dissemination, and the need for predictive maintenance. In this survey, while highlighting the key benefits of using blockchain-based DTs, we present a comprehensive review of the state-of-the-art research results for blockchain-based DTs. Based on the current research trends, we discuss a trustworthy blockchain-based DTs framework. We also highlight the role of artificial intelligence in blockchain-based DTs. Furthermore, we discuss the current and future research and deployment challenges of blockchain-supported DTs that require further investigation.</p

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Resource-constrained, scalable learning

textOur unprecedented capacity for data generation and acquisition often reaches the limits of our data storage capabilities. Situations when data are generated faster or at a greater volume than can be stored demand a streaming approach. Memory is an even more valuable resource. Algorithms that use more memory than necessary can pose bottlenecks when processing high-dimensional data and the need for memory-efficient algorithms is especially stressed in the streaming setting. Finally, network along with storage, emerge as the critical bottlenecks in the context of distributed computation. These computational constraints spell out a demand for efficient tools that guarantee a solution in the face of limited resources, even when the data is very noisy or highly incomplete. For the first part of this dissertation, we present our work on streaming, memory-limited Principal Component Analysis (PCA). Therein, we give the first convergence guarantees for an algorithm that solves PCA in the single-pass streaming setting. Then, we discuss the distinct challenges that arise when the received samples are overwhelmingly incomplete and present an algorithm and analysis that deals with this issue. Finally, we give a set of extensive experiment results that showcase the practical merits of our algorithm over the state of the art. The need for heavy network communication arises as the bottleneck when dealing with cluster computation. In that paradigm, a set of worker nodes are connected over the network to produce a cluster with improved computational and storage capacities. This comes with an increased need for communication across the network. In the last part of this work, we consider the problem of PageRank on graph engines. Therein, we make changes to GraphLab, a state-of-the-art platform for distributed graph computation, in a way that leads to a 7x-10x speedup for certain PageRank approximation tasks. Accompanying analysis supports the behaviour we see in our experiments.Electrical and Computer Engineerin

Predictive maintenance of electrical grid assets: internship at EDP Distribuição - Energia S.A

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis report will describe the activities developed during an internship at EDP Distribuição, focusing on a Predictive Maintenance analytics project directed at high voltage electrical grid assets including Overhead Lines, Power Transformers and Circuit Breakers. The project’s main goal is to support EDP’s asset management processes by improving maintenance and investing planning. The project’s main deliverables are the Probability of Failure metric that forecast asset failures 15 days ahead of time, estimated through supervised machine learning models; the Health Index metric that indicates asset’s current state and condition, implemented though the Ofgem methodology; and two asset management dashboards. The project was implemented by an external service provider, a consultant company, and during the internship it was possible to integrate the team, and participate in the development activities

Automated anomaly recognition in real time data streams for oil and gas industry.

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and other engineering industries. To reduce the reliance on field experts' knowledge for identification of these anomalies, this thesis proposes a deep-learning anomaly-detection framework that can help to create an effective real-time condition-monitoring framework. The aim of this research is to develop a real-time and re-trainable generic anomaly-detection framework, which is capable of predicting and identifying anomalies with a high level of accuracy - even when a specific anomalous event has no precedent. Machine-based condition monitoring is preferable in many practical situations where fast data analysis is required, and where there are harsh climates or otherwise life-threatening environments. For example, automated conditional monitoring systems are ideal in deep sea exploration studies, offshore installations and space exploration. This thesis firstly reviews studies about anomaly detection using machine learning. It then adopts the best practices from those studies in order to propose a multi-tiered framework for anomaly detection with heterogeneous input sources, which can deal with unseen anomalies in a real-time dynamic problem environment. The thesis then applies the developed generic multi-tiered framework to two fields of engineering: data analysis and malicious cyber attack detection. Finally, the framework is further refined based on the outcomes of those case studies and is used to develop a secure cross-platform API, capable of re-training and data classification on a real-time data feed

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Study on quality in 3D digitisation of tangible cultural heritage: mapping parameters, formats, standards, benchmarks, methodologies and guidelines: final study report.

This study was commissioned by the Commission to help advance 3D digitisation across Europe and thereby to support the objectives of the Recommendation on a common European data space for cultural heritage (C(2021) 7953 final), adopted on 10 November 2021. The Recommendation encourages Member States to set up digital strategies for cultural heritage, which sets clear digitisation and digital preservation goals aiming at higher quality through the use of advanced technologies, notably 3D. The aim of the study is to map the parameters, formats, standards, benchmarks, methodologies and guidelines relating to 3D digitisation of tangible cultural heritage. The overall objective is to further the quality of 3D digitisation projects by enabling cultural heritage professionals, institutions, content-developers, stakeholders and academics to define and produce high-quality digitisation standards for tangible cultural heritage. This unique study identifies key parameters of the digitisation process, estimates the relative complexity and how it is linked to technology, its impact on quality and its various factors. It also identifies standards and formats used for 3D digitisation, including data types, data formats and metadata schemas for 3D structures. Finally, the study forecasts the potential impacts of future technological advances on 3D digitisation