Development of a Framework for Improvement of Sensor Data Quality from Weather Buoys
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초록
In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in re- al-world scenarios.
Abstract
In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in re- al-world scenarios.
참고문헌 (25)
- Abdel-Aal, R. E., "Hourly temperature forecasting using abductive networks", Engineering Applications of Artificial Intelligence, 17(5), pp.543~556 (2004)
- Barnett, V., "Outliers in statistical data", pp.7~33 (1994), Wiley New York
- Bidlot, J. R., "Intercomparison of the performance of operational ocean wave forecasting systems with buoy data", Weather and Forecasting, 17(2), pp.287~310 (2002)
- Chakraborty, D., "Advanced machine learning techniques for building performance simulation : a comparative analysis", Journal of Building Performance Simulation, 12(2), pp.193~207 (2019)
- Cifuentes, J., "Air temperature forecasting using machine learning techniques : A review", Energies, 13(16), pp.4215~ (2020)
- Dai, X., "signal to knowledge : A data-driven perspective of fault detection and diagnosis", IEEE Transactions on Industrial Informatics, 9(4), pp.2226~2238 (2013)
- Duan, T., "Ngboost: Natural gradient boosting for probabilistic prediction", International Conference on Machine Learning, pp.2690~2700 (2020)
- Ebuchi, N., "Evaluation of wind vectors observed by QuikSCAT", SeaWinds using ocean buoy data, 2002, pp.19(12), 2049~2062
- Fang, Z., "Multi-zone indoor temperature prediction with LSTM-based sequence to sequence mod-el", Energy and Buildings, 245, pp.111053~ (2021)
- Ge, Z., "Review of recent research on data-based process monitoring", Industrial & Engineer-ing Chemistry Research, 52(10), pp.3543~3562 (2013)
- Heidemann, J., "Research challenges and applications for underwater sen-sor networking", IEEE Wireless Communications and Networking Conference 2006, WCNC, pp.228~235 (2006), IEEE
- Hochreiter, S., "Long short-term memory", Neural Computation, 9(8), pp.1735~1780 (1997)
- Jeong, S., "Temperature prediction based on bidirectional long short-term memory and convolutional neural network combining observed and numerical forecast data", Sensors, 21(3), pp.941~ (2021)
- Khan, M. I., "Hybrid deep learning approach for multi-step-ahead prediction for daily maximum tem-perature and heatwaves", Theoretical and Applied Climatology, 149(3-4), pp.945~963 (2022)
- Leonard, N. E., "Collective motion, sensor networks, and ocean sampling", Proceedings of the IEEE, 95(1), pp.48~74 (2007)
- Li, D., "Recent advances in sensor fault diagnosis: A review", Sensors and Actuators A: Physical, 309, pp.111990~ (2020)
- Liu, J., "Convolutional LSTM networks for seawater temper-ature prediction", 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) (2019), IEEE
- Qin, S. J., "Survey on data-driven industrial process mon-itoring and diagnosis", Annual Reviews in Control, 36(2), pp.220~234 (2012)
- Reynolds, R. W., "A real-time global sea surface temper-ature analysis", Journal of Climate, 1(3), pp.75~87 (1988)
- Venkatesan, R., "Analysis of drift characteristic in conductivity and temperature sensors used in Moored buoy system", Ocean Engineering, 171, pp.151~156 (2019)
- Wang, R., "Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings", Sustainable Cities and Society, 49, pp.101623~ (2019)
- Xiang, L., "Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism", Measurement, 175, pp.109094~ (2021)
- Yin, S., "Data-driven design of robust fault detection system for wind turbines", Mechatronics, 24(4), pp.298~306 (2014)
- Zamora-Martinez, F., "On-line learning of indoor temperature forecasting models towards energy efficiency", Energy and Buildings, 83, pp.162~172 (2014)
- Zhang, D., "A data-driven design for fault detection of wind turbines using random forests and XGboost", IEEE Access, 6, pp.21020~21031 (2018)