Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

Hang, Jun; Zhang, Jianzhong; Cheng, Ming

doi:10.1016/j.fss.2015.07.005

Cited by 70 publications

(28 citation statements)

References 26 publications

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“…Five algorithms, namely neural networks, multivariate adaptive regression splines, support vector machines, k-nearest neighbors, and linear regression, were applied to model the wind turbine power output, drivetrain vibratory acceleration, and tower vibratory acceleration based on training and sampled datasets. Reference [18] presented an approach for fault diagnosis in wind turbines based on a multi-class fuzzy support vector machine classifier. Reference [19] investigated a data-driven fault detection and isolation design based on the fusion of several classifiers for a wind turbine benchmark second challenge.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability. Compared with Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other fault diagnosis methods, the average diagnostic correctness rate is higher; so, the method can provide more accurate reference information for wind turbine fault diagnosis.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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“…FSVM was also used to solve multi-classification problems [79]. Hang, et al [80], employed EEMD to extract fault feature vectors, and FSVM was adopted to solve multi-classification problems in fan fault diagnosis. Comparison of FSVM classification results with backpropagation and standard SVM indicated that FSVM had higher classification accuracy.…”

Section: Svmmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…Unsupervised learning is sometimes applied as a way to obtain labels for training classifiers or to derive some parameters of the classification models [12,13]. Some unsupervised [14][15][16] and semi-supervised problems [17,18] have also arisen in the context of RE, but the analysis made in this paper is mainly contextualized on supervised classification techniques.…”

Section: Classification Problems: An Important Part Of Machine Learnimentioning

confidence: 99%

“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

Section: Reference Year Application Fieldmentioning

confidence: 99%

“…An accuracy of 98% was obtained using a linear SVM with eight output classes (no fault, five different impairments and two different misalignments). In [14], a multi-class fuzzy SVM is proposed for this problem. Data are obtained from vibration signals and then processed using Empirical Mode Decomposition (EMD), a self-adaptive processing method suitable for non-stationary signals, which attempts to overcome some of the limitations of the wavelet transform (e.g., border distortion, interference terms, energy leakage and choice of wavelet basis).…”

Section: Classification Problems and Algorithms In Fault Diagnosis Inmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

Cited by 70 publications

References 26 publications

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

A Review of Classification Problems and Algorithms in Renewable Energy Applications

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