Mining and fusing data for ocean turbine condition monitoring

An ocean turbine extracts 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.;Our research investigates the feasibility of various data mining, machine learning and data fusion techniques for an MCM/PHM system. Studies conducted on experimental data aim to reveal the optimal approach for fusing and interpreting sensor data. Also considered in these studies is the possibility of imperfect data and other challenges that could negatively affect the efficiency of our techniques. Specifically, we assess the robustness of our techniques to changing environmental conditions, class imbalance (i.e., the relative lack of fault data as compared to data collected during normal operation that will be available to construct state detection models) and data incompleteness (e.g. missing values in the data).;This dissertation proposes an MCM/PHM software architecture employing those techniques which were determined from these 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.