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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation

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arXiv 2021年

作者： Steger, Sophie Geiger, Bernhard C. Śmieja, Marek Signal Processing and Speech Communication Laboratory Graz University of Technology Inffeldgasse 16c Graz8010 Austria Know-Center GmbH Inffeldgasse 13 Graz8010 Austria Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Krakow Poland

We connect the problem of semi-supervised clustering to constrained Markov aggregation, i.e., the task of partitioning the state space of a Markov chain. We achieve this connection by considering every data point in the dataset as an element of the Markov chain's state space, by defining the transition probabilities between states via similarities between corresponding data points, and by incorporating semi-supervision information as hard constraints in a Hartigan-style algorithm. The introduced Constrained Markov Clustering (CoMaC) is an extension of a recent information-theoretic framework for (unsupervised) Markov aggregation to the semi-supervised case. Instantiating CoMaC for certain parameter settings further generalizes two previous information-theoretic objectives for unsupervised clustering. Our results indicate that CoMaC is competitive with the state-of-the-art. Furthermore, our approach is less sensitive to hyperparameter settings than the unsupervised counterpart, which is especially attractive in the semi-supervised setting characterized by little labeled data. Copyright © 2021, The Authors. All rights reserved.

关键词： Information theory

Minimal Binary Linear Codes from Vectorial Boolean Functions

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arXiv 2022年

作者： Li, Yanjun Peng, Jie Kan, Haibin Zheng, Lijing Institute of Statistics and Applied Mathematics Anhui University of Finance & Economics Anhui Bengbu233030 China Mathematics and Science College of Shanghai Normal University Guilin Road #100 Shanghai200234 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Engineering Research Center of Blockchain Shanghai Institute for Advanced Communication and Data Science Shanghai200433 China School of Mathematics and Physics University of South China Hunan Hengyang421001 China

Recently, much progress has been made to construct minimal linear codes due to their preference in secret sharing schemes and secure two-party computation. In this paper, we put forward a new method to construct minimal linear codes by using vectorial Boolean functions. Firstly, we give a necessary and sufficient condition for a generic class of linear codes from vectorial Boolean functions to be minimal. Based on that, we derive some new three-weight minimal linear codes and determine their weight distributions. Secondly, we obtain a necessary and sufficient condition for another generic class of linear codes from vectorial Boolean functions to be minimal and to be violated the AB condition. As a result, we get three infinite families of minimal linear codes violating the AB condition. To the best of our knowledge, this is the first time that minimal liner codes are constructed from vectorial Boolean functions. Compared with other known ones, in general the minimal liner codes obtained in this paper have higher dimensions. © 2022, CC BY-NC-SA.

关键词： Boolean functions

Uncertainty measurement for hybrid data using K N N -neighbourhood rough set model: Application to attribute reduction based on overlap degree

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applied Soft Computing 2025年 179卷

作者： Bozhan Li Qin Huang Zhaowen Li Yonghua Lin School of Mathematics and Information Science Guangxi University Nanning Guangxi 530004 PR China Key Laboratory of Applied Mathematics in Fujian Province University Fujian Key Laboratory of Financial Information Processing Putian University Putian Fujian 351100 PR China School of Computer Science Guangdong University of Science and Technology Dongguan Guangdong 523083 PR China

Neighbourhood rough set ( N -rough set) model is extensively applied to work with numerical data. However, most N -rough set models cannot effectively classify categories when they are used to depict classification ability. k -nearest neighbour rule ( K N N rule) is an important classification technique. This paper introduces K N N -neighbourhood rough set model by combining N -rough set model with K N N rule, investigates uncertainty measurement (UM) for hybrid data using this model, and considers its application to attribute reduction based on overlap degree. First, a specific distance function is constructed to describe the difference between two information values with respect to each attribute in a hybrid information system (HIS), the distance matrix is defined to establish the neighbourhood relation on the object set, and the neighbourhood classes where a variable parameter is selected to adjust the size of the neighbourhood classes are built. N -rough set model is introduced using the neighbourhood classes. The k -nearest neighbour in an HIS is then proposed on the basis of K N N rule, and K N N -neighbourhood rough set model is presented by combining N -rough set model with the k -nearest neighbour. On the basis of K N N -neighbourhood rough set model, the information granules in an HIS are constructed, and four UMs in an HIS are presented. Next, an experiment is carried out to select the UM with the optimal performance. Furthermore, an attribute reduction algorithm is designed using the selected UM. Finally, the designed algorithm is evaluated using a series of experiments implemented on real-world datasets. The experimental results demonstrate the statistical advantages of the designed algorithm over the other six reduction algorithms.

关键词： k-nearest neighbors

Anisotropic and Isotropic Implicit Obstacle Problems with Nonlocal Terms and Multivalued Boundary Conditions

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SSRN

SSRN 2022年

作者： Zeng, Shengda Gasinski, Leszek Radulescu, Vicentiu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Department of Mathematics Pedagogical University of Cracow Podchorazych 2 Cracow30-084 Poland Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania China-Romania Research Center in Applied Mathematics Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper we study an anisotropic implicit obstacle problem driven by the $(p(\cdot),q(\cdot))$-Laplacian and an isotropic implicit obstacle problem involving a nonlinear convection term (a reaction term depending on the gradient) which contain several interesting and challenging untreated problems. These two implicit obstacle problems have both highly nonlinear and nonlocal functions and three multivalued terms where two of them are appearing on the boundary and the other one is formulated in the domain. Under very general assumptions on the data, we develop general frameworks to examine the nonemptiness and compactness of the set of weak solutions to the problems under consideration. The proofs of our main results use the theory of nonsmooth analysis, Tychonoff's fixed point theorem for multivalued operators, the theory of pseudomonotone operators and variational approach. © 2022, The Authors. All rights reserved.

关键词： Anisotropy

Transfer learning with convolutional networks for atmospheric parameter retrieval: A preprint

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arXiv 2020年

作者： Malmgren-Hansen, David Nielsen, Allan Aasbjerg Laparra, Valero Camps-Valls, Gustau Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Image Processing Laboratory Universitat de València València Spain

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform well on this task, but both schemes are computationally heavy on large quantities of data. Kernel methods do not scale well with the number of training data, and neural networks require setting critical hyperparameters. In this work we follow an alternative pathway: we study transfer learning in convolutional neural nets (CNNs) to alleviate the retraining cost by departing from proxy solutions (either features or networks) obtained from previously trained models for related variables. We show how features extracted from the IASI data by a CNN trained to predict a physical variable can be used as inputs to another statistical method designed to predict a different physical variable at low altitude. In addition, the learned parameters can be transferred to another CNN model and obtain results equivalent to those obtained when using a CNN trained from scratch requiring only fine tuning. © 2020, CC BY-NC-ND.

关键词： Convolution

Spatial noise-aware temperature retrieval from infrared sounder data

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arXiv 2020年

In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using MNF instead. In our analysis we also investigate the relationship between error rate improvements when including more spectral and spatial components in the regression model, aiming to uncover the trade-off between model complexity and error rates. © 2020, CC BY-NC-ND.

关键词： Principal component analysis

A predictive surrogate model for heat transfer of an impinging jet on a concave surface

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International Journal of Heat and Mass Transfer 2025年 251卷

作者： Sajad Salavatidezfouli Saeed Rakhsha Armin Sheidani Giovanni Stabile Gianluigi Rozza Mathematics Area MathLab International School for Advanced Studies (SISSA) Trieste Italy Department of Electrical and Computer Engineering - Signal Processing and Machine Learning Aarhus University Aarhus Denmark Department of Mechanical Engineering Semnan University Semnan Iran Department of Applied Physics Eindhoven University of Technology The Netherlands Department of Pure and Applied Sciences Informatics and Mathematics Section University of Urbino Carlo Bo Urbino Italy The Biorobotics Institute Sant’Anna School of Advanced Studies Pisa Italy

This paper aims to comprehensively investigate the efficacy of model order reduction and deep learning techniques in predicting heat transfer of pulsatile impinging jets on a concave surface. We introduce two predictive approaches: one employing a Fast Fourier Transform-augmented Artificial Neural Network for predicting the area-averaged Nusselt number under constant-frequency jet scenarios, and another comparing the performance of LSTM and Transformer neural networks for random-frequency jets. Results indicate that the Transformer significantly outperforms the LSTM, achieving higher accuracy and robustness in predicting Nusselt numbers — covering up to 50% of the cycle with precision, whereas the LSTM covers only 20% with greater error margins. Additionally, the integration of Proper Orthogonal Decomposition with Transformer networks yields a novel strategy for predicting local Nusselt number distributions. This method significantly reduces computational complexity while maintaining high accuracy, with a maximum prediction error of 5%. These findings demonstrate the efficacy of advanced deep learning techniques for temporal prediction of the Nusselt number on complex surfaces, suggesting further applicability in broader fluid dynamics and heat transfer analyses.

关键词： Concave surface Deep learning Heat transfer Model order reduction Predictive surrogate model

A class of elliptic mixed boundary value problems with (p,q)-Laplacian: existence, comparison and optimal control

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arXiv 2023年

作者： Zeng, Shengda Migórski, Stanislaw Tarzia, Domingo A. Zou, Lang Nguyen, Van Thien Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30348 Poland College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University in Krakow Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Depto. Matemática FCE Universidad Austral Paraguay 1950 RosarioS2000FZF Argentina CONICET Argentina School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City210000 China Department of Mathematics FPT University Education zone Hoa Lac High Tech Park Km29 Thang Long Highway Thach That ward Hanoi Viet Nam

The paper deals with two nonlinear elliptic equations with (p, q)-Laplacian and the Dirichlet-Neumann-Dirichlet (DND) boundary conditions, and Dirichlet-Neumann-Neumann (DNN) boundary conditions, respectively. Under mild hypotheses, we prove the unique weak solvability of the elliptic mixed boundary value problems. Then, a comparison and a monotonicity results for the solutions of elliptic mixed boundary value problems are established. We examine a convergence result which shows that the solution of (DND) can be approached by the solution of (DNN). Moreover, two optimal control problems governed by (DND) and (DNN), respectively, are considered, and an existence result for optimal control problems is obtained. Finally, we provide a result on asymptotic behavior of optimal controls and system states, when a parameter tends to *** Codes 35J25, 35J66, 35J92, 49J20, 35Bxx © 2023, CC BY.

关键词： Nonlinear equations