Fault Diagnosis of a Wind Turbine Benchmark via Statistical and Support Vector Machine

2018 ◽  
Vol 37 ◽  
pp. 29-42 ◽  
Author(s):  
Abderrahmane Mokhtari ◽  
Mohammed Belkheiri
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Wind Turbine ◽  
Moving Average ◽  
Fault Detection And Isolation ◽  
Support Vector ◽  
Svm Classifier ◽  
Actuator Faults ◽  
Active System ◽  
Benchmark Model

This paper addresses the problem of fault detection and isolation (FDI) in wind turbine benchmark model using data driven and multi-class support vector machine (SVM) approach. Since, the fault detection is fundamental for any active system, isolation is similarly vital, and identification is decisive for fault reconfiguration as well as maintenance addition to monitoring purposes. The need for man-made dynamic system to work automatically when sensor, actuator, or system faults occur was constantly developed in order to increase reliability and decrease unavailability and maintenance costs. The key step of our approach based on extraction of mean features from sensors measurements by applying the statistical methods such as moving standard deviation and the exponential weighted moving average (EWMA). The fault detection step is invoked later based on the multi-class SVM classifier that decides the presence or not of the fault. Another important contribution of this paper is the simulation of combined sensor and actuator faults simultaneously for the first time in wind turbine benchmark model. The FDI performances are illustrated through simulation study for seven different scenario tests. The results demonstrate clearly the effectiveness of statistical and SVM approach to detect and isolate single, multiple sensor and actuator faults and outperforms many results reported in the literature for solving this problem.

scholarly journals Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 460 ◽  
Author(s):  
Zuojun Liu ◽  
Cheng Xiao ◽  
Tieling Zhang ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Cost Effective ◽  
Support Vector ◽  
Detection Accuracy ◽  
Machine Method ◽  
Scada System

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

scholarly journals Fault detection and isolation of pitch actuator faults in a floating wind turbine

2018 ◽  
Vol 51 (24) ◽  
pp. 480-487
Author(s):  
Christian Tutivén ◽  
Yolanda Vidal ◽  
Leonardo Acho ◽  
José Rodellar
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Fault Detection And Isolation ◽  
Actuator Faults ◽  
Floating Wind Turbine

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

2021 ◽  
Vol 39 (5A) ◽  
pp. 859-869
Author(s):  
Ahmed M. Abed ◽  
Sabah A. Gitaffa ◽  
Abbas H. Issa
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Nearest Neighbor ◽  
Process Variable ◽  
Fault Detection And Isolation ◽  
Support Vector ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Safety And Reliability ◽  
Sensor Fault Detection

Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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