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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM
Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art.
Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM
Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art.
Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM
Zheng Wang (Autor:in) / Peng Gao (Autor:in) / Xuening Chu (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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