A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

Janell Duhaney, T. Khoshgoftaar, J. Sloan, B. Alhalabi, P. Beaujean
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引用次数: 6

Abstract

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 a retrained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machine'sstate. 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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海洋涡轮机原型机的测功机:通过自动监测的可靠性
海洋涡轮机从洋流中提取动能来发电。机器状态监测(MCM) /预测健康监测(PHM)系统允许自检和自动故障检测,并且是高可靠性海洋涡轮机结构中不可或缺的一部分。本文介绍了一个海洋涡轮机的陆上测试平台,以及一个案例研究,展示了如何使用机器学习来根据其振动信号检测该工厂运行状态的变化。在案例研究中,七个广泛使用的机器学习者通过从测试平台(一个测力计)收集的实验数据进行再训练,以检测机器状态的变化。由这些分类器生成的分类模型被认为是海洋涡轮机MCM/PHM系统状态检测模块的可能组成部分,并将用于故障预测。实验结果表明,基于小波变换预处理的振动数据,决策树和随机森林学习器能够有效区分故障和正常状态。
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