Submerged turbines which harvest energy from ocean currents are an important potential energy resource, but their harsh and remote environment demands an automated system for machine condition monitoring and prognostic health monitoring (MCM/PHM). For building MCM/PHM models, vibration sensor data is among both the most useful (because it can show abnormal behavior which has yet to cause damage) and the most challenging (because due to its waveform nature, frequency bands must be extracted from the signal).
To perform the necessary analysis of the vibration signals, which may arrive rapidly in the form of data streams, we develop three new wavelet-based transforms (the Streaming Wavelet Transform, Short-Time Wavelet Packet Decomposition, and Streaming Wavelet Packet Decomposition) and propose modifications to the existing Short-Time Wavelet Transform. We also prepare post-processing techniques to resolve additional problems such as interpreting wavelet data in a fully-streaming format, automatically choosing the appropriate transformation depth without performing classification, and building models which can perform state identification correctly even as the turbine’s environment changes. Collectively, these new approaches solve problems not currently dealt with by existing algorithms and offer important improvements. The proposed algorithms allow for data to be processed in a fully-streaming manner. These algorithms also create and select frequency-band features which focus on the areas of the signal most important to MCM/PHM, producing only the information necessary for building models (or removing all unnecessary information) so models can run on less powerful hardware. Finally, we demonstrate models which can work in multiple environmental conditions.
To evaluate these algorithms, along with the Short-Time Fourier Transform which is often neglected in the context of MCM/PHM, we perform six case studies on data from two different physical machines, a fan and a dynamometer model of the ocean turbine. Our results show that many of the transforms give similar results in terms of performance, but their different properties as to time complexity, ability to operate in a fully streaming fashion, and number of generated features may make some more appropriate than others in particular applications, such as when streaming data or hardware limitations are extremely important (e.g., ocean turbine MCM/PHM).
|Advisor:||Khoshgoftaar, Taghi M.|
|Commitee:||Alhalabi, Bassem, Hanson, Howard, Solomon, Martin K.|
|School:||Florida Atlantic University|
|Department:||Computer and Electrical Engineering and Computer Science|
|School Location:||United States -- Florida|
|Source:||DAI-B 74/06(E), Dissertation Abstracts International|
|Subjects:||Alternative Energy, Ocean engineering, Computer science|
|Keywords:||Ocean turbines, Submerged turbines, Turbine reliability, Vibration, Wavelength transform|
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