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The increasing system design complexity is negatively impacting the overall system design productivity by increasing the cost and time of product development. One key to overcoming these challenges is exploiting Component Based Engineering practices. Components are designed for various specifications and usually variants of each component exist with different performance and quality of service parameters. Therefore it is a challenge to select the optimum components from a component library that will satisfy all functional and non-functional system requirements. If these designs and architectural decisions are delayed, they may lead to design re-spins thereby, negatively impacting the product development cost and time. In this paper we propose an integrated framework for component selection.

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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