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Mining and fusing data for ocean turbine condition monitoring
Title
Mining and fusing data for ocean turbine condition monitoring.
Creator
Duhaney, Janell A., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
An ocean turbine extarcts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. MCM/PHM systems enable real time health assessment, prognostics and advisory generation by interpreting data from sensors installed on the machine being monitored. To effectively utilize sensor...
Show moreAn ocean turbine extarcts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. MCM/PHM systems enable real time health assessment, prognostics and advisory generation by interpreting data from sensors installed on the machine being monitored. To effectively utilize sensor readings for determining the health of individual components, macro-components and the overall system, these measurements must somehow be combined or integrated to form a holistic picture. The process used to perform this combination is called data fusion. Data mining and machine learning techniques allow for the analysis of these sensor signals, any maintenance history and other available information (like expert knowledge) to automate decision making and other such processes within MCM/PHM systems. ... This dissertation proposes an MCM/PHM software architecture employing those techniques which were determined from the experiments to be ideal for this application. Our work also offers a data fusion framework applicable to ocean machinery MCM/PHM. Finally, it presents a software tool for monitoring ocean turbines and other submerged vessels, implemented according to industry standards.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3358556
Subject Headings
Marine turbines, Mathematical models, Fluid dynamics, Data mining, Machine learning, Multisensor data fusion
Format
Document (PDF)
Sensitivity analysis of predictive data analytic models to attributes
Title
Sensitivity analysis of predictive data analytic models to attributes.
Creator
Chiou, James, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we...
Show moreClassification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004274, http://purl.flvc.org/fau/fd/FA00004274
Subject Headings
Data mining, Forecasting -- Mathematical models, Social sciences -- Statistical methods, Ubiquitous computing
Format
Document (PDF)
Pattern mining and visualization for molecular dynamics simulation
Title
Pattern mining and visualization for molecular dynamics simulation.
Creator
Kong, Xue, Zhu, Xingquan, Florida Atlantic University, College of Computer Science and Engineering, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Molecular dynamics is a computer simulation technique for expressing the ultimate details of individual particle motions and can be used in many fields, such as chemical physics, materials science, and the modeling of biomolecules. In this thesis, we study visualization and pattern mining in molecular dynamics simulation. The molecular data set has a large number of atoms in each frame and range of frames. The features of the data set include atom ID; frame number; position in x, y, and z...
Show moreMolecular dynamics is a computer simulation technique for expressing the ultimate details of individual particle motions and can be used in many fields, such as chemical physics, materials science, and the modeling of biomolecules. In this thesis, we study visualization and pattern mining in molecular dynamics simulation. The molecular data set has a large number of atoms in each frame and range of frames. The features of the data set include atom ID; frame number; position in x, y, and z plane; charge; and mass. The three main challenges of this thesis are to display a larger number of atoms and range of frames, to visualize this large data set in 3-dimension, and to cluster the abnormally shifting atoms that move with the same pace and direction in different frames. Focusing on these three challenges, there are three contributions of this thesis. First, we design an abnormal pattern mining and visualization framework for molecular dynamics simulation. The proposed framework can visualize the clusters of abnormal shifting atom groups in a three-dimensional space, and show their temporal relationships. Second, we propose a pattern mining method to detect abnormal atom groups which share similar movement and have large variance compared to the majority atoms. We propose a general molecular dynamics simulation tool, which can visualize a large number of atoms, including their movement and temporal relationships, to help domain experts study molecular dynamics simulation results. The main functions for this visualization and pattern mining tool include atom number, cluster visualization, search across different frames, multiple frame range search, frame range switch, and line demonstration for atom motions in different frames. Therefore, this visualization and pattern mining tool can be used in the field of chemical physics, materials science, and the modeling of biomolecules for the molecular dynamic simulation outcomes.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004212, http://purl.flvc.org/fau/fd/FA00004212
Subject Headings
Data mining, Information visualization, Molecular dynamics -- Computer simulation, Molecules -- Mathematical models, Pattern perception
Format
Document (PDF)
Analyzing software repository data to synthesize and visualize relationships between development artifacts
Title
Analyzing software repository data to synthesize and visualize relationships between development artifacts.
Creator
Mulcahy, James J., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
As computing technology continues to advance, it has become increasingly difficult to find businesses that do not rely, at least in part, upon the collection and analysis of data for the purpose of project management and process improvement. The cost of software tends to increase over time due to its complexity and the cost of employing humans to develop, maintain, and evolve it. To help control the costs, organizations often seek to improve the process by which software systems are developed...
Show moreAs computing technology continues to advance, it has become increasingly difficult to find businesses that do not rely, at least in part, upon the collection and analysis of data for the purpose of project management and process improvement. The cost of software tends to increase over time due to its complexity and the cost of employing humans to develop, maintain, and evolve it. To help control the costs, organizations often seek to improve the process by which software systems are developed and evolved. Improvements can be realized by discovering previously unknown or hidden relationships between the artifacts generated as a result of developing a software system. The objective of the work described in this thesis is to provide a visualization tool that helps managers and engineers better plan for future projects by discovering new knowledge gained by synthesizing and visualizing data mined from software repository records from previous projects.
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Date Issued
2011
PURL
http://purl.flvc.org/FAU/3333053
Subject Headings
Data mining, Mathematical models, Software engineering, Inofrmation visualization, Data processing, Application software, Development, Object-oriented programming (Computer science)
Format
Document (PDF)