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.

Wind turbine failure prediction and health assessment based on adaptive maximum mean discrepancy

2022 ◽  
Vol 134 ◽  
pp. 107391
Author(s):  
Jieyang Peng ◽  
Andreas Kimmig ◽  
Zhibin Niu ◽  
Jiahai Wang ◽  
Xiufeng Liu ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Health Assessment ◽  
Failure Prediction ◽  
Maximum Mean Discrepancy

Wind Turbine Failure Prediction Model using SCADA-based Condition Monitoring System

2021 ◽  
Author(s):  
Bara Alzawaideh ◽  
Payam Teimourzadeh Baboli ◽  
Davood Babazadeh ◽  
Susanne Horodyvskyy ◽  
Isabel Koprek ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Failure Prediction ◽  
Condition Monitoring System

Assessment of Current Health of Hard Disk Drives

2010 ◽  
Author(s):  
Abe Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Health Management ◽  
State Of The Art ◽  
Hard Disk Drives ◽  
Health Assessment ◽  
Hard Disk ◽  
Assessment Method ◽  
Hard Disks ◽  
Disk Drives ◽  
Future Directions ◽  
Self Monitoring

Prognostics and health management of computer hard disk drives is beneficial from two different angles: it can help computer users plan for timely replacement of HDDs before they catastrophically fail and cause serious data loss; it can also help product recover facilities reuse hard disks recovered from the end-of-life computers for building refurbished computers. This paper presents a HDD health assessment method using Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. It also presents the state-of-the art results in monitoring the condition of hard disks and offers future directions for distributed hard disk monitoring.

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 Comparison of Electrical and Vibration Analysis Methods for Mechanical Fault Monitoring in Wind Turbine Drivetrains

2019 ◽  
Vol 9 (1) ◽  
pp. 2-7
Author(s):  
P Granjon ◽  
P D Longhitano ◽  
A Singh
Keyword(s):  
Wind Turbine ◽  
Vibration Analysis ◽  
Wind Farm ◽  
Electromechanical System ◽  
Main Bearing ◽  
Monitoring Method ◽  
Fault Monitoring ◽  
Three Phase ◽  
Mechanical Faults ◽  
Electrical Generator

Mechanical faults occurring in drivetrains are traditionally monitored through vibration analysis and, more rarely, by analysing electrical quantities measured on the electromechanical system involved. However, a monitoring method that is able to take into account all of the information contained in three-phase electrical quantities was recently proposed. The goal of this paper is to compare this threephase electrical approach with the usual vibration-based method in terms of its capability to detect mechanical faults in drivetrains. In this context, a 2 MW geared wind turbine operating in an industrial wind farm was equipped with accelerometers near the main bearing and electrical sensors on the stator of the electrical generator for several months. During this period, an important mechanical fault occurred in the main bearing of the system. The evolution of the fault indicators computed by the two previous approaches were compared throughout this period of time. All of the indicators behaved similarly and showed the development of an inner bearing fault in the main bearing. This demonstrated that a mechanical fault occurring in a drivetrain can be monitored and detected by analysing electrical quantities, even if the fault is located some distance from the electrical generator.

scholarly journals Value-Centric Approaches to Design, Operations, and Maintenance of Wind Turbines

2013 ◽  
Author(s):  
Madhur A. Khadabadi ◽  
Karen B. Marais
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Net Present Value ◽  
Operating Cost ◽  
Optimal Level ◽  
Two Dimensions ◽  
Alternative View ◽  
Operations And Maintenance ◽  
Farm Operators

Wind turbine maintenance is emerging as an unexpectedly high component of turbine operating cost and there is an increasing interest in managing this cost. Here, we present an alternative view of maintenance as a value-driver, and develop an optimization algorithm to maximize the value delivered by maintenance. We model the stochastic deterioration of the turbine in two dimensions: the deterioration rate, and the extent of deterioration, and view maintenance as an operator that moves the turbine to an improved state in which it can generate more power and so earn more revenue. We then use a standard net present value (NPV) approach to calculate the value of the turbine by deducting the costs incurred in the installation, operations and maintenance from the revenue due to the power generation. The application of our model is demonstrated using several scenarios with a focus on blade deterioration. We evaluate the value delivered by implementing blade condition monitoring systems (CMS). A higher fidelity CMS allows the blade state to be determined with higher precision. With this improved state information, an optimal maintenance strategy can be derived. The difference between the value of the turbine with and without CMS can be interpreted as the value of the CMS. The results indicate that a higher fidelity (and more expensive) condition monitoring system (CMS) does not necessarily yield the highest value, and, that there is an optimal level of fidelity that results in maximum value. The contributions of this work are twofold. First, it is a practical approach to wind turbine valuation and operation that takes operating and market conditions into account. This work should therefore be useful to wind farm operators and investors. Second, it shows how the value of a CMS can be explicitly assessed. This work should therefore be useful to CMS manufacturers and wind farm operators.

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.

scholarly journals Feature Selection Algorithms for Wind Turbine Failure Prediction

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 453 ◽  
Author(s):  
Pere Marti-Puig ◽  
Alejandro Blanco-M ◽  
Juan Cárdenas ◽  
Jordi Cusidó ◽  
Jordi Solé-Casals
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Wind Turbine ◽  
Wind Farm ◽  
Feature Selection Method ◽  
Failure Prediction ◽  
Exhaustive Search ◽  
Input Selection ◽  
Selection Algorithms ◽  
Optimal Set

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.

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