As wind energy is experiencing an unprecedented development in today's world, the condition monitoring of wind turbine systems which can avoid serious accidents and economic losses are gathering more and more attentions. Considering the evaluation methods based on machine learning are complicated and unstable in terms of model training and parameter selection, this paper proposed a novel assessment algorithm based on similarity analysis of fuzzy k ‐principal curves (FKPCs) in manifold space. Initially, 38 fusion features containing time‐domain, frequency‐domain, and wavelet node energy features are extracted from the vibration signal of the wind turbine bearing. Then, the nonlinear local algorithm Laplacian eigenmaps was introduced to transform the original features to the projected lower dimensional space and obtain the more typical parameters. Combining fuzzy clustering with the k ‐principal curve creatively, smooth principal curves of the feature sets were then extracted according to point distribution in three‐dimensional manifold space. Finally, the Hausdorff distance algorithm was employed to calculate the similarity between the principal curves of healthy and test datasets to assess the running condition of wind turbine. To validate the present method, the experiments of accelerated bearing degradation were carried out. A series of comprehensive and persuasive comparisons and analysis with different feature conversion methods (principal component analysis [PCA] and Isomap), different distance analysis algorithms (Euclidean distance and Mahalanobis distance), and different assessment models (hidden Markov model [HMM] and deep belief network [DBN]) verified that the proposed FKPC technique can monitor the operating status of the machine more accurately without complex model training and parameter selection processes.

Retracted: Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring

This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment.

https://www.mdpi.com/2076-3417/8/12/2611/pdf

Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

This study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic-mechanical-electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time-based maintenance to transform to the condition-based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy-Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.383

Bayesian network approach to fault diagnosis of a hydroelectric generation system

Notice of Retraction. Manifold learning-based fuzzy k-principal curve similarity evaluation for wind turbine condition monitoring

A quantity of particular experimental interest is the forward-backward asymmetry in the angular distribution of positively and negatively charged fermions produced in Z 0 decays. Measurements of this asymmetry can enable independent determinations of the neutral-current couplings of these fermions, i.e. the Z 0 boson couplings for left- and right-handed fermions, respectively. Due to the quark conﬁnement, however, it is di ﬃ cult to determine the electroweak interactions of quarks, especially for light quarks. In the hadron production electron positron annihilation process, the parton model with factorization theorem gives a reliable approximate description. Quantities are thus expressed in terms of fragmentation functions in the annihilation process. In this paper, we consider the vector meson production in the inclusive electron positron annihilation process and calculate the forward-backward asymmetry in the hadronic level. Calculations are carried out by applying the collinear expansion in the parton model at leading order twist-4. We note here this process provides not only a tool for analyzing the hadronic weak interactions but also an opportunity for understanding the parton model of the strong interaction. In other words, the results can be used to test the electroweak and strong interactions simultaneously.

http://link.aps.org/pdf/10.1103/PhysRevD.106.036016

Forward-backward asymmetry in the electron positron annihilation process at twist-4

The hadron pair forward-backward asymmetry in the electron positron annihilation process

pdf/the-nuclear-matter-effect-on-parton-distribution-functions-x4y05452ofib.pdf

The nuclear matter effect on parton distribution functions

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Notice of Retraction. Manifold learning-based fuzzy k-principal curve similarity evaluation for wind turbine condition monitoring

Forward-backward asymmetry in the electron positron annihilation process at twist-4

The hadron pair forward-backward asymmetry in the electron positron annihilation process

The nuclear matter effect on parton distribution functions