Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

Zhong, Jiajun; Zhang, Jun; Liang, Jiejunyi; Wang, Haiqing

doi:10.1109/access.2018.2885816

Cited by 31 publications

(28 citation statements)

References 44 publications

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“…For example, the HL and TB functions provide poor results for the BPSK and QPSK modes, respectively. If the equalizer outcome will be a real number, these observations could be attributed to the testing root mean square error, which is commonly used to quantify the effectiveness of any classifier or regressors [10][11][12][14][15][16][17]20,41,42]. Unfortunately, the proposed phase-noise suppression technique provides an output in the complex plane (the points in the constellation).…”

Section: Parameters' Optimization Of the Extreme Learning Machinementioning

confidence: 99%

“…Therefore, the output weights in the training process simplify into a regularized least problem, and of which result comes from a closed equation. Enhancements and extensions of the standard ELM such as improving the stability [11] and compactness [12] of the ELM, ELMs for online sequential [13] and imbalanced [14] data, Bayesian [15], fuzzy [16], Wavelet [17], or complex [18,19] ELMs have been demonstrated excellent generation performance, superior efficiency, and less computational complexity, by giving solutions for regression and classification problems in diverse practical areas (medical, remote sensing, control and robotics, image/video processing, time series analysis, text classification and understanding, telecommunication, chemical process, and computer vision). Hence, the issues of convergence ability, generalization, over-fitting, local minima, and/or parameter adjustment (learning rate, learning epochs, among others) presented in support vector machines as well as back-propagation artificial neural networks do not happen in ELMs [10,20].…”

Section: Introductionmentioning

confidence: 99%

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Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine

et al. 2020

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A coherent optical (CO) orthogonal frequency division multiplexing (OFDM) scheme gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, as any coherent-detection OFDM system, the overall system performance is limited by laser phase noises. On the other hand, extreme learning machines (ELMs) have gained a lot of attention from the machine learning community owing to good generalization performance, negligible learning speed, and minimum human intervention. In this manuscript, a phase-error mitigation method based on the single-hidden layer feedforward network prone to the improved ELM algorithm for CO-OFDM systems is introduced for the first time. In the training step, two steps are distinguished. Firstly, pilots are used, which is very common in OFDM-based systems, to diminish laser phase noises as well as to correct frequency-selective impairments and, therefore, the bandwidth efficiency can be maximized. Secondly, the regularization parameter is included in the ELM to balance the empirical and structural risks, namely to minimize the root mean square error in the test stage and, consequently, the bit error rate (BER) metric. The operational principle of the real-complex (RC) ELM is analytically explained, and then, its sub-parameters (number of hidden neurons, regularization parameter, and activation function) are numerically found in order to enhance the system performance. For binary and quadrature phase-shift keying modulations, the RC-ELM outperforms the benchmark pilot-assisted equalizer as well as the fully-real ELM, and almost matches the common phase error (CPE) compensation and the ELM defined in the complex domain (C-ELM) in terms of the BER over an additive white Gaussian noise channel and different laser oscillators. However, both techniques are characterized by the following disadvantages: the CPE compensator reduces the transmission rate since an additional preamble is mandatory for channel estimation purposes, while the C-ELM requires a bounded and differentiable activation function in the complex domain and can not follow semi-supervised training. In the same context, the novel ELM algorithm can not compete with the CPE compensator and C-ELM for the 16-ary quadrature amplitude modulation. On the other hand, the novel ELM exposes a negligible computational cost with respect to the C-ELM and PAE methods.

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Section: Parameters' Optimization Of the Extreme Learning Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine

et al. 2020

View full text Add to dashboard Cite

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“…Indeed, the operation of WEC systems is usually accompanied by unexpected faults, which should be detected and classified at an early stage to avoid a system collapse. The wind variability, vibrations, and mainly the power electronics interfaces remain the main sources of failures [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

et al. 2020

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Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.

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“…that aim at reducing WTs' O&M costs such as condition monitoring (CM), non-destructive testing (NDT) [5], fault diagnosis (FD) [6], etc.…”

Section: Introductionmentioning

confidence: 99%

A Wind Turbine Bearing Performance Evaluation Method Based on Similarity Analysis of Fuzzy k-Principal Curves in Manifold Space

et al. 2019

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Condition monitoring (CM) is widely used in wind turbines (WTs) to reduce operation and maintenance (O&M) costs. Bearings are crucial components in WT and many bearing CM approaches have focused on vibration analysis. Statistical theory and artificial intelligence-based WT bearings evaluation methods require mass data for training, which makes the detection of incipient failures barely possible. In this paper, a WT bearing performance evaluation method is proposed based on the similarity analysis of fuzzy k-principal curves (FKPCs) in manifold space. For a start, 38 features are extracted from bearing vibration signals to constitute high-dimensional feature matrices. The feature matrices for the healthy samples and the samples to be evaluated are then transformed into 3-D space. Afterward, the FKPCs are extracted and the similarities among the curves of samples are calculated based on the Hausdorff distance to evaluate the performance of the bearing. Bearing degradation experiments are investigated to verify the efficiency of the proposed method. The results indicate that the proposed FKPC method can portray the degradation trend of bearings accurately with the capability of detecting incipient failures. The proposed method can be applied in the case of small-size training samples with stable performance. INDEX TERMS Wind farms, condition monitoring, fault diagnosis, prognostics and health management.

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Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

Cited by 31 publications

References 44 publications

Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine

Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

A Wind Turbine Bearing Performance Evaluation Method Based on Similarity Analysis of Fuzzy k-Principal Curves in Manifold Space

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