Wind Turbine Planetary Gearbox Condition Monitoring Method Based on Wireless Sensor and Deep Learning Approach

2021 ◽  
Vol 70 ◽  
pp. 1-16
Author(s):  
Li Lu ◽  
Yigang He ◽  
Yi Ruan ◽  
Weibo Yuan
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wireless Sensor ◽  
Learning Approach ◽  
Monitoring Method ◽  
Planetary Gearbox

scholarly journals Wind Turbine Planetary Gearbox Fault Diagnosis Based on Self-Powered Wireless Sensor and Deep Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 119430-119442 ◽  
Author(s):  
Li Lu ◽  
Yigang He ◽  
Tao Wang ◽  
Tiancheng Shi ◽  
Yi Ruan
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wireless Sensor ◽  
Learning Approach ◽  
Planetary Gearbox ◽  
Self Powered

scholarly journals Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

2018 ◽  
Vol 198 ◽  
pp. 04008
Author(s):  
Zhongshan Huang ◽  
Ling Tian ◽  
Dong Xiang ◽  
Sichao Liu ◽  
Yaozhong Wei
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Copula Function ◽  
Single Step ◽  
Fusion Model ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.

scholarly journals Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57078-57087 ◽  
Author(s):  
Jian Fu ◽  
Jingchun Chu ◽  
Peng Guo ◽  
Zhenyu Chen
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Model ◽  
Wind Turbine Gearbox ◽  
Deep Learning Model

scholarly journals Three-Stage Wind Turbine Assessment Method: Condition Monitoring, Failure Prediction, And Health Assessment

2021 ◽  
Author(s):  
Yongsheng Qi ◽  
Tongmei Jing ◽  
Chao Ren ◽  
Xuejin Gao
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Health Management ◽  
Health Assessment ◽  
Failure Prediction ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Generalized Regression Neural Network ◽  
Monitoring Method

Abstract To improve the wind turbine shutdown early warning ability, we present a generalized model for wind turbine (WT) prognosis and health management (PHM) based on the data collected from the SCADA system. First, a new condition monitoring method based on kernel entropy component analysis (KECA) was developed for nonlinear data. Then, an aggregate statistic T was designed to express the state change of the monitoring parameters. As the features were submerged because of the diversity and nonlinearity of SCADA data, an enhanced generalized regression neural network (GRNN) method—KECA-GRNN—for failure prediction was developed by adding KECA for feature extraction to improve the predictive performance. Finally, the results of the KECA-GRNN model were visualized by a bubble chart, which made the health assessment results of the WT more intuitive. Similarly, the fusion residual was defined to analyze the health trend of the WT, and the health status of the WT was represented by two visualization methods—bubble chart and fuzzy comprehensive evaluation. Furthermore, they were evaluated using SCADA data that were collected from a wind farm. Observations from the results of the model indicated the ability of the approach to trend and assess turbine degradation before known downtime occurrences.

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