Current Search: Multisensor data fusion (x)
View All Items
- 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.
Show less - 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)
- Title
- Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
- Creator
- Dabbas, Mohammad A., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.ď€ Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
- Subject Headings
- Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
- Format
- Document (PDF)