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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.

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)

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)

This dissertation describes the development, design, implementation and evaluation of a data acquisition system, with the main aim of using it for data collection on a laboratory pilot ball mill. An open-source prototype hardware platform was utilised in the implementation of the data acquisition function, however, with limitations. An analogue signal conditioning card has been successfully developed to interface the analogue signals to the dual domain ADC module. Model-based software development was used to design and develop the algorithms to control the DAS acquisition process, but with limited capabilities. A GUI application has been developed and used for the collection and storage of the raw data on the host system. The DAS prototype was calibrated and collected data successfully through all the channels; however, the input signal bandwidth was limited to 2Hz. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Indiana University-Purdue University Indianapolis (IUPUI) / One of the most common goals of a wireless sensor network is to collect sensor data. The goal of this thesis is to provide an easy to use and energy-e fficient system for deploying data collection sensor networks. There are numerous challenges associated with deploying a wireless sensor network for collection of sensor data; among these challenges are reducing energy consumption and the fact that users interested in collecting data may not be familiar with software design. This thesis presents a complete system, comprised of the Compression Data-stream Protocol and a general gateway for data collection in wireless sensor networks, which attempts to provide an easy to use, energy efficient and complete system for data collection in sensor networks. The Compressed Data-stream Protocol is a transport layer compression protocol with a primary goal, in this work, to reduce energy consumption. Energy consumption of the radio in wireless sensor network nodes is expensive and the Com-pressed Data-stream Protocol has been shown in simulations to reduce energy used on transmission and reception by around 26%. The general gateway has been designed in such a way as to make customization simple without requiring vast knowledge of sensor networks and software development. This, along with the modular nature of the Compressed Data-stream Protocol, enables the creation of an easy to deploy and easy to configure sensor network for data collection. Findings show that individual components work well and that the system as a whole performs without errors. This system, the components of which will eventually be released as open source, provides a platform for researchers purely interested in the data gathered to deploy a sensor network without being restricted to specific vendors of hardware.

Text in English / In this study, the research problem sought to explore, identify and acknowledge the value of vehicle tracking technology within the South African Police Service (SAPS). National legislation in the Republic of South Africa allows the SAPS and private organisations, such as Tracker, to create partnerships to successfully combat crime, such as vehicle related crimes. Data was collected by means of a literature study, together with semi-structured interviews that were individually conducted with non-commissioned officers of the SAPS: West Rand Flying Squad. These members are deployed daily, in an operational environment, to deal with the recovery of stolen and robbed motor vehicles; they utilise vehicle tracking technology to fulfil this function. A detailed study of literature relating to national legislation, SAPS directives, media and newspaper reports as well as library resources and international studies was conducted. The research indicates the importance of vehicle tracking technology in assisting specialized units within the SAPS to successfully and efficiently track and locate stolen or robbed motor vehicles. It is evident that the use of this type of technology has become an invaluable tool to the SAPS: West Rand Flying Squad members in their daily duties. Furthermore, members at grassroots level understand and appreciate the assistance and value of technology, especially as the technology enables them to effectively recover stolen or robbed motor vehicles, and to successfully arrest the perpetrators responsible for these thefts. The recommendations made in this study may provide a number of solutions to the South African government, SAPS, insurance industry and the general public, regarding the value of vehicle tracking technology. In addition, the study indicates how this technology can effectively assist in curbing vehicle crimes and the recovery of stolen or robbed motor vehicles; in the process, recovery affects the arrests of criminals, thus saving the economy a significant amount of money due to crimes of this nature. / Police Practice / M.A. (Criminal Justice)