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During the historical data collection period (of some given time series data about wind turbine bearing temperature), several wind turbines had generator bearing problems. Four wind turbines had generator bearing failures and replacements. The symptom of bearing fault is rising temperature beyond normal range. The task is to build the ML model to detect anomaly in generator bearing and identify wind turbines that shows generator bearing defect. You are required to submit the following: List of WT’s that are suspected to have a generator bearing defect during the data period including 4 that had change out, Result showing the reason for diagnosis, and the code associated with the aforementioned. You are then given ids for wind turbines that had NO generator bearing defect (healthy).
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Machine Learning Engineer

Interviewed at ONYX InSight

3.9★
Mar 26, 2019

During the historical data collection period (of some given time series data about wind turbine bearing temperature), several wind turbines had generator bearing problems. Four wind turbines had generator bearing failures and replacements. The symptom of bearing fault is rising temperature beyond normal range. The task is to build the ML model to detect anomaly in generator bearing and identify wind turbines that shows generator bearing defect. You are required to submit the following: List of WT’s that are suspected to have a generator bearing defect during the data period including 4 that had change out, Result showing the reason for diagnosis, and the code associated with the aforementioned. You are then given ids for wind turbines that had NO generator bearing defect (healthy).

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