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The development of structural monitoring systems is a critical research area because of the age and sustainment costs associated with many aircraft in use today. Specifically, integrated structural health monitoring (SHM) systems are advantageous because they allow for automated, near real-time assessment of the state of the structure, where the automation improves both the accuracy of the measurements and allows for more frequent system interrogation than possible with traditional nondestructive evaluation methods. Ultrasonic techniques are particularly well-suited for SHM systems because of their potential to detect and track damage well before structural failure using in situ sensors. The research problem considered in this thesis is detection and tracking of fatigue cracks emanating from fastener holes in metallic structural components. The sensing method utilizes attached ultrasonic transducers, and tracking of damage is achieved by employing a state estimation framework that incorporates a well-known empirical model for crack growth and a measurement model relating the ultrasonic response to crack size. The state estimation process is preceded by an automated crack detection algorithm, and can be followed by a prediction of remaining life assuming future usage. The state estimation framework provides a better estimate of crack size than either the ultrasonic measurement model or crack growth model alone. Although the example application is monitoring of fastener holes, the general approach is applicable to a variety of SHM problems.

This thesis describes environmental data collection and transmission from the field to a server using Wi-Fi. Also discussed are components, radio wave propagation, received power calculations, and throughput tests. Measured receive power resulted close to calculated and simulated values. Throughput tests resulted satisfactory. The thesis provides detailed systematic procedures for Wi-Fi radio link setup and techniques to optimize the quality of a radio link.

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...

Thesis: S.M., Joint Program in Applied Ocean Physics and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 97-107). / Mobile robots and autonomous sensors have seen increasing use in scientific applications, from planetary rovers surveying for signs of life on Mars, to environmental buoys measuring and logging oceanographic conditions in coastal regions. This thesis makes contributions in both planning algorithms and model design for autonomous scientific information gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in scientific data, and have probabilistic performance guarantees. This thesis first introduces a novel sample selection algorithm for online, irrevocable sampling in data streams that have spatiotemporal structure, such as those that commonly arise in robotics and environmental monitoring. Given a limited sampling capacity, the proposed periodic secretary algorithm uses an information-theoretic reward function to select samples in real-time that maximally reduce posterior uncertainty in a given scientific model. Additionally, we provide a lower bound on the quality of samples selected by the periodic secretary algorithm by leveraging the submodularity of the information-theoretic reward function. Finally, we demonstrate the robustness of the proposed approach by employing the periodic secretary algorithm to select samples irrevocably from a seven-year oceanographic data stream collected at the Martha's Vineyard Coastal Observatory off the coast of Cape Cod, USA. Secondly, we consider how scientific models can be specified in environments - such as the deep sea or deep space - where domain scientists may not have enough a priori knowledge to formulate a formal scientific model and hypothesis. These domains require scientific models that start with very little prior information and construct a model of the environment online as observations are gathered. We propose unsupervised machine learning as a technique for science model-learning in these environments. To this end, we introduce a hybrid Bayesian-deep learning model that learns a nonparametric topic model of a visual environment. We use this semantic visual model to identify observations that are poorly explained in the current model, and show experimentally that these highly perplexing observations often correspond to scientifically interesting phenomena. On a marine dataset collected by the SeaBED AUV on the Hannibal Sea Mount, images of high perplexity in the learned model corresponded, for example, to a scientifically novel crab congregation in the deep sea. The approaches presented in this thesis capture the depth and breadth of the problems facing the field of autonomous science. Developing robust autonomous systems that enhance our ability to perform exploratory science in environments such as the oceans, deep space, agricultural and disaster-relief zones will require insight and techniques from classical areas of robotics, such as motion and path planning, mapping, and localization, and from other domains, including machine learning, spatial statistics, optimization, and theory of experimental design. This thesis demonstrates how theory and practice from these diverse disciplines can be unified to address problems in autonomous scientific information gathering. / by Genevieve Elaine Flaspohler. / S.M.

The automation of industrial processes has been one of the greatest innovations in the industrial sector. It allows faster and accurate operations of production processes while producing more outputs than old manual production techniques. In the beverage industry, this innovation was also well embraced, especially to improve its bottling processes. However it has been proven that a continuous optimization of automation techniques using advanced and current trend of automation is the only way industrial companies will survive in a very competitive market. This becomes more challenging for small to medium scale enterprises (SMEs) which are not always keen in adopting new technologies by fear of overspending their little revenues. By doing so, SMEs are exposing themselves to limited growth and vulnerable lifecycle in this fast growing automation world. The main contribution of this study was to develop practical and affordable applications that will optimize the bottling process of a SME beverage plant by combining its existing production resources to basic principles of the current trend of automation, Industry 4.0 (I40). This research enabled the small beverage industry to achieve higher production rate, better delivery time and easy access of plant information through production forecast using linear regression, predictive maintenance using speed vibration sensor and decentralization of production monitoring via cloud applications. The existing plant Siemens S7-1200 programmable logic controller (PLC) and ZENON supervisory control and data acquisition (SCADA) system were used to program the optimized process with very few additional resources. This study also opened doors for automation in SMEs, in general, to use I40 in their production processes with available means and limited cost. / School of Computing / M.Tech (Engineering, Electrical)

Sandvik Rock Tools is operating in a highly competitive market. This requires them to constantly improve the efficiency of their organization, in order for them to maintain their position as market leaders, both in a short-term and a long-term perspective. Sandvik Rock Tools is currently working on developing and further improving their supply chain. The supply chain is a key organizational function and improvements of the supply chain aim to yield benefits like lowered total costs and better utilized inventory levels. The purpose of our work has been to develop the supply chain between Sandvik’s in-market warehouse and their customer site at Dannemora. The main focus of this thesis has been to enable automatic data collection and to lower the stock levels at the customer site in Dannemora. This would be accomplished by improving the information flow between the two entities in the observed supply chain and by enabling real-time inventory information, easily accessible to the warehouse manager. A barcode system was implemented in the warehouse at the Dannemora customer site and was integrated with warehouse management software that was developed by the authors. The implemented barcode system and the warehouse management software were used at the Dannemora customer site for a period of two months. The effects of the implemented barcode system working in combination with the warehouse management software were observed in several operational areas after implementation, and the results were positive and beneficial. Firstly, the barcode system and warehouse management software simplified the warehouse manager’s decision process, by providing easy access to necessary warehouse management information, like realtime inventory information. Secondly, it made the day-to-day work routine of the warehouse manager easier, this being the case because the barcode system that had been implemented had digitalized and automated many tasks that had previously been performed manually by warehouse manager. An excellent example would be the simplified process for order placement and stocktaking. Thirdly, it improved the information flow between the Dannemora customer site and Sandvik’s in-market warehouse in Sandviken, thereby improving the overall effectiveness of the entire supply chain, through shared information. The barcode implementation successfully lowered inventory levels by six percent on average.

In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity. Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.

A thesis which models the DNP3 and IEC 61850 protocol in OPNET / Supervisory Control and Data Acquisition (SCADA) is a critical part of monitoring and controlling of the electrical substation. The aim of this dissertation is to investigate the performance of the Distributed Network Protocol Version 3.3 (DNP3) protocol and to compare its performance to that of International Electro-technical Commission (IEC) 61850 protocol in an electrical substation communication network environment. Building an electrical substation control room and installing the network equipment was going to be expensive and take a lot of time. The better option was to build a model of the electrical substation communication network and run simulations. Riverbend modeller academic edition known as Optimized Network Engineering Tool (OPNET) was chosen as a software package to model substation communication network, DNP3 protocol and IEC 61850 Protocol stack. Modelling the IEC 61850 protocol stack on OPNET involved building the used Open System Interconnection (OSI) layers of the IEC 61850 protocol stack onto the application definitions of OPNET. The Transmission Control Protocol/Internet Protocol (TCP/IP) configuration settings of DNP3 protocol were also modelled on the OPNET application definitions. The aim is to compare the two protocols and determine which protocol is the best performing one in terms of throughput, data delay and latency. The substation communication model consists of 10 ethernet nodes which simulate protection Intelligent Electronic Devices (IEDs), 13 ethernet switches, a server which simulates the substation Remote Terminal Unit (RTU) and the DNP3 Protocol over TCP/IP simulated on the model. DNP3 is a protocol that can be used in a power utility computer network to provide communication service for the grid components. DNP3 protocol is currently used at Eskom as the communication protocol because it is widely used by equipment vendors in the energy sector. DNP3 protocol will be modelled before being compared to the new recent robust protocol IEC 61850 in the same model and determine which protocol is the best for Eskom on the network of the power grid. The network load and packet delay parameters were sampled when 10%, 50%, 90% and 100% of devices are online. The IEC 61850 protocol model has three scenarios and they are normal operation of a Substation, maintenance in a Substation and Buszone operation at a Substation. In these scenarios packet end to end delay of Generic Object Oriented Substation Event (GOOSE), vi © University of South Africa 2020 Generic Substation Status Event (GSSE), Sampled Values (SV) and Manufacturing Messaging Specification (MMS) messages are monitored. The throughput from the IED under maintenance and the throughput at the Substation RTU end is monitored in the model. Analysis of the results of the DNP3 protocol simulation showed that with an increase in number of nodes there was an increase in packet delay as well as the network load. The load on the network should be taken into consideration when designing a substation communication network that requires a quick response such as a smart gird. GOOSE, GSSE, SV results on the IEC 61850 model met all the requirements of the IEC 61850 standard and the MMS did not meet all the requirements of the IEC standard. The design of the substation communication network using IEC 61850 will assist when trying to predict the behavior of the network with regards to this specific protocol during maintenance and when there are faults in the communication network or IED’s. After the simulation of the DNP3 protocol and the IEC 61850 the throughput of DNP3 protocol was determined to be in the range (20 – 450) kbps and the throughput of IEC61850 protocol was determined to be in the range (1.6 – 16) Mbps. / College of Engineering, Science and Technology / M. Tech. (Electrical Engineering)