A novel wind turbine health condition monitoring method based on common features distribution adaptation

2020 ◽  
Vol 44 (11) ◽  
pp. 8681-8688 ◽  
Author(s):  
Wenyi Liu ◽  
He Ren
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Health Condition ◽  
Monitoring Method ◽  
Common Features

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 Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

2017 ◽  
Vol 231 (5) ◽  
pp. 481-494 ◽  
Author(s):  
Peyman Mazidi ◽  
Mian Du ◽  
Lina Bertling Tjernberg ◽  
Miguel A Sanz Bobi
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Real Time ◽  
Condition Monitoring ◽  
Health Condition ◽  
Hazard Models ◽  
Proportional Hazard ◽  
Neural Network Models ◽  
Proportional Hazard Models ◽  
Condition Model

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 981-984
Author(s):  
Jian Guo Cui ◽  
Can Wu ◽  
Li Ying Jiang ◽  
Yi Wen Qi ◽  
Guo Qiang Li
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Probabilistic Neural Network ◽  
Complex Structure ◽  
Reference Value ◽  
Health Condition ◽  
Monitoring Method ◽  
Accurate Localization ◽  
Safety And Reliability ◽  
Path System

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

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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