This paper identifies factors that impact reliability and safety of ocean turbines. We describe how physical and environmental factors will impact the design of its machine condition monitoring (MCM) system. Environmental factors like fouling, corrosion, and inaccessibility of equipment sets this MCM problem apart from those encountered by wind turbines, hydroelectric plants, or even ship hulls and propellers. Fouling constitutes the primary and most persistent source of failure. In addition to compromising turbine efficiency and reliability, fouling reduces sensor data quality — masking faults that will ultimately lead to failure. Unmitigated fouling triggers a form of biological succession known as flocculation that may eventually attract threatened species of tortoises and cetaceans to this rotating machinery. We review and suggest refinements to a class of non-toxic biologically-inspired anti-fouling techniques known as engineered topographies. Advances in this area will enable turbines to operate in portions of the water column that maximize momentum flux while minimizing retrieval cost.

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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. This paper presents an onshore test platform for an ocean turbine as well as a case study showing how machine learning can be used to detect changes in the operational state of this plant based on its vibration signals.In the case study, seven widely used machine learners are trained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machine's state. The classification models generated by these classifiers are being considered as possible components of the state detection module of an MCM/PHM system for ocean turbines, and would be used for fault prediction. Experimental results presented here show the effectiveness of decision tree and random forest learners on distinguishing between faulty and normal states based on vibration data preprocessed by a wavelet transform.

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