Tidal power is an emerging technology with great potential to provide a sustainable means of renewable energy in many areas worldwide. However, the nature of the underwater environment provides challenges. Submerged machinery cannot be easily accessed for inspections, and turbines must be brought to the surface for maintenance. This is an expensive process and results in prolonged periods of downtime where no power can be supplied to the grid. Condition monitoring systems, capable of accurately and remotely assessing the health state of machinery while in operation, can therefore be of great value to this industry. This paper presents an approach for condition monitoring of a tidal turbine’s gearbox from monitoring data with low sample rates. Models of normal behavior were trained using weighted least squares regression, where prediction errors are used to identify changes in response. This paper then examines how prediction errors from a number of different cases (including changes in control scheme and simulated gearbox faults) can be interpreted by operators to classify anomalous behavior.

中文翻译：

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通过加权最小二乘回归建模和解释潮汐涡轮机振动

潮汐能是一种新兴技术，具有在全球许多地区提供可持续的可再生能源方式的巨大潜力。然而，水下环境的性质带来了挑战。浸入水中的机械无法轻易进入进行检查，涡轮机必须被带到水面进行维护。这是一个昂贵的过程，会导致长时间停机，无法向电网供电。因此，状态监测系统能够准确地远程评估运行中的机器的健康状态，因此对该行业具有重要价值。本文提出了一种通过低采样率监测数据对潮汐涡轮机齿轮箱进行状态监测的方法。使用加权最小二乘回归训练正常行为模型，其中预测误差用于识别响应的变化。然后，本文研究了操作员如何解释来自许多不同情况（包括控制方案的变化和模拟齿轮箱故障）的预测误差，以对异常行为进行分类。