scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Large Scale ◽  
Support Vector ◽  
Effective Response ◽  
Support Vector Regression Model ◽  
Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

scholarly journals Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1671 ◽  
Author(s):  
Chia-Hung Yeh ◽  
Min-Hui Lin ◽  
Chien-Hung Lin ◽  
Cheng-En Yu ◽  
Mei-Juan Chen
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Sensor Data ◽  
Hybrid Network ◽  
Support Vector ◽  
Significant Information ◽  
Long Cycle ◽  
Power Company ◽  
Maintenance Time

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

scholarly journals A new hybrid model of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine for fault diagnosis of gear pump

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402092204
Author(s):  
Yan Lu ◽  
Zhiping Huang
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Support Vector ◽  
Gear Pump ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Adaptive Dynamic ◽  
Empirical Wavelet Transform

Gear pump is the key component in hydraulic drive system, and it is very significant to fault diagnosis for gear pump. The combination of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine is proposed for fault diagnosis of gear pump in this article. Sparsity empirical wavelet transform is used to obtain the features of the vibrational signal of gear pump, the sparsity function is potential to make empirical wavelet transform adaptive, and adaptive dynamic least squares support vector machine is used to recognize the state of gear pump. The experimental results show that the diagnosis accuracies of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine are better than those of the empirical wavelet transform and adaptive dynamic least squares support vector machine method or the empirical wavelet transform and least squares support vector machine method.

Fault Diagnosis Platform Development Based on Wavelet Transform for Wind Turbines Transmission System

2013 ◽  
Vol 333-335 ◽  
pp. 632-635
Author(s):  
Zhao Ran Hou ◽  
Wu Wang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Complex Structure ◽  
Vibration Signal ◽  
Transmission System ◽  
Measurement Unit ◽  
Diagnosis System ◽  
Fault Features

Wind turbine transmission system with complex structure and abundant fault features, also the fault features are variable, three typical fault and failure forms of rolling bearings, shafts and gears in wind turbine drive train were analyzed, also the failure mechanism and corresponding vibration signal characteristics was proposed. In the wind turbines transmission system, the vibration signal can reflect most of the fault information, as there was non-stationary signals in the vibration signals and wavelet transform for feature extraction was proposed. The whole framework of the fault diagnosis platform was constructed, the lower machine based on the measurement unit and the PC based host computer diagnosis system was designed, the diagnosis system was powerful and easily cut, which was strong help to the fault character extraction, also it was practical and available for wind turbine fault diagnosis.

scholarly journals Fault Diagnosis of Belt Conveyor Based on Support Vector Machine and Grey Wolf Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiangong Li ◽  
Yu Li ◽  
Yuzhi Zhang ◽  
Feng Liu ◽  
Yu Fang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Diagnostic Model ◽  
Grey Wolf ◽  
Belt Conveyor

Belt conveyor is widely used for material transportation over both short and long distances nowadays while the failure of a single component may cause fateful consequences. Accordingly, the use of machine learning in timely fault diagnosis is an efficient way to ensure the safe operation of belt conveyors. The support vector machine is a powerful supervised machine learning algorithm for classification in fault diagnosis. Before the classification, the principal component analysis is used for data reduction according to the varieties of features. To optimize the parameters of the support vector machine, this paper presents a grey wolf optimizer approach. The diagnostic model is applied to an underground mine belt conveyor transportation system fault diagnosis on the basis of monitoring data collected by sensors of mine internet of things. The results show that the recognition accuracy of the fault is up to 97.22% according to the mine site dataset. It is proved that the combined classification model has a better performance in fault intelligent diagnosis.

scholarly journals SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021 ◽  
Vol 11 (8) ◽  
pp. 3307
Author(s):  
Francesco Castellani ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Principal Component ◽  
Support Vector ◽  
General Context ◽  
Test Case ◽  
Electric Generator ◽  
Before And After ◽  
Electrical Faults

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

scholarly journals Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting

2020 ◽  
Vol 10 (9) ◽  
pp. 3258 ◽  
Author(s):  
Zidong Wu ◽  
Xiaoli Wang ◽  
Baochen Jiang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Fault Classification ◽  
Gradient Boosting ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Scada System ◽  
Extreme Gradient Boosting ◽  
Multi Classification

In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.

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