Mining and fusing data for ocean turbine condition monitoring

• Other Authors: Duhaney, Janell A.
• Format: Others
• Language: English
• Published: Florida Atlantic University
• Subjects: Marine turbines > Mathematical models; Fluid dynamics; Data mining; Machine learning; Multisensor data fusion
• Online Access: http://purl.flvc.org/FAU/3358556

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 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. === by Janell A. Duhaney. === Thesis (Ph.D.)--Florida Atlantic University, 2012. === Includes bibliography. === Mode of access: World Wide Web. === System requirements: Adobe Reader.