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

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

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An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

arXiv

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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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Machine Learning based Multiscale Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Multiscale Reduced Kernel PCA for Non...

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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Khaled Dhibi Radhia Fezai Kais Bouzrara Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Laboratory of Automatic Signal and Image Processing School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection (FD) is fundamental for monitoring several chemical processes. Thus, this paper introduces a novel structure multiscale reduced kernel principal component analysis (MS-RKPCA). The proposed FD method aims to address the problem of great computation time and significant storage memory space by using a data reduction structure based on the Euclidean distance metric. Additionally, to further enhance the RKPCA method, a multiscale representation of data will be used. The enhanced MS-RKPCA method uses the wavelet coefficients of the reduced data at each scale to enhance the fault detection results. The detection performance of the proposed MS-RKPCA method is evaluated using the Tennessee Eastman Process (TEP). The effectiveness of the enhanced method is evaluated in terms of the missed detection rates (MDR), false alarms rates (FAR) and computation time (CT). The results demonstrate that the developed technique is more effective for fault detection mostly in terms of computation time and memory storage space.

关键词： Fault detection Computational modeling Euclidean distance Wavelet analysis Kernel Principal component analysis Wavelet coefficients

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Fault detection of uncertain nonlinear process using interval-valued data-driven approach

Fault detection of uncertain nonlinear process using interva...

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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Khaled Dhibi Radhia Fezai Kais Bouzrara Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Kernel PCA (KPCA) has been extensively applied in fault detection (FD) field. However, it is constantly not optimal for uncertain systems and is not designed to handle large-scale process monitoring. Thus, a nonlinear fault detection (FD) method based interval reduced KPCA (IRKPCA) is developed for fault detection. The proposed IRKPCA technique uses interval-valued Euclidean distance as a criterion to maintain only the more pertinent measurements. The FD abilities of the IRKPCA technique is assessed using the Tennessee Eastman Process (TEP). The effectiveness of the proposed technique is assessed in terms of computation time (CT), false alarm rate (FAR)and missed detection rate (MDR).

关键词： Uncertain systems Uncertainty Fault detection Tools Nonlinear systems Kernel Principal component analysis

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Machine Learning based Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Reduced Kernel PCA for Nonlinear Proc...

引用

2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Dhibi, Khaled Fezai, Radhia Mansouri, Majdi Kouadri, Abdelmalek Bouzrara, Kais Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

Process monitoring is an essential part of industrial systems. It requires higher product quality and safety operations. Therefore, a nonlinear data-driven approach based reduced KPCA (RKPCA) for statistical monitoring of industrial processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction and supervised learning. The use of classical KPCA for modeling and monitoring purposes can impose a high computational load when a large number of measurements are recorded. The main idea of the proposed RKPCA approach is to reduce the number of observations (samples) in the data matrix using the Euclidean distance between samples as dissimilarity metric so that only one observation is kept in case of redundancy. The Tennessee Eastman Process (TEP) is used to evaluate the fault detection abilities of the proposed RKPCA technique. The performance of the proposed method is evaluated and compared to the classical KPCA in terms of false alarms rates (FAR), missed detection rates (MDR) and computation times (CT). © 2019 IEEE.

关键词： Fault detection

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Reduced Gaussian process regression for fault detection of chemical processes

Reduced Gaussian process regression for fault detection of c...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Fezai, Radhia Mansouri, Majdi Bouguila, Nasreddine Nounou, Hazem Nounou, Mohamed Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar Chemical Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

In this paper, a reduced Gaussian process regression (RGPR)-based generalized likelihood ratio test (GLRT) is proposed for fault detection in industrial systems. In contrast to the classical GPR technique, the RGPR model can handle domains with a large number of activities that require very large training sets. The developed RGPR-based GLRT method aims first to build a RGPR model, then, it consists to apply GLRT to the monitored residuals obtained from PGPR for fault detection purposes. The fault detection performance of the developed RGPR-based GLRT method is illustrated through the Tennessee Eastman process. The simulation results show that the RGPR-based GLRT method outperforms the conventional GPR-based GLRT technique in terms of miss detection rate and CPU-time. © 2019 IEEE.

关键词： Fault detection

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Machine learning based Gaussian process regression for fault detection of Biological Systems

Machine learning based Gaussian process regression for fault...

引用

2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

ISBN: (纸本)9781728151847

Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine. © 2019 IEEE.

关键词： Fault detection

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An overview of deep learning in medical imaging

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Informatics in Medicine Unlocked 2021年 26卷

作者： Anaya-Isaza, Andrés Mera-Jiménez, Leonel Zequera-Diaz, Martha Pontificia Universidad Javeriana Bogotá 110231 Colombia Faculty of Engineering Bioengineering Program Universidad de Antioquia Medellín 050010 Colombia School of Engineering BASPI/FootLab (Bioengineering Signal Analysis And Image Processing Research Group) Pontificia Universidad Javeriana Bogotá 110231 Colombia INDIGO Research Bogotá 110221 Colombia

Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growth in recent years. The scientific community has focused its attention on DL due to its versatility, high performance, high generalization capacity, and multidisciplinary uses, among many other qualities. In addition, a large amount of medical data and the development of more powerful computers has also fostered an interest in this area. This paper presents an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence. In the first instance, the fundamental concept of artificial neural networks is introduced, progressively covering convolutional structures, recurrent networks, attention models, up to the current structure known as the Transformer. Secondly, all the basic concepts involved in training and other common elements in the design of the architectures are introduced. Thirdly, some of the key elements in modern networks for medical image classification and segmentation are shown. Subsequently, a review of some applications realized in the last years is shown, where the main features related to DL are highlighted. Finally, the perspectives and future expectations of deep learning are presented. © 2021 The Authors

关键词： Attention models Convolutional neural networks Deep learning Medical imaging Recurrent neural networks Transformers

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Enhanced Machine Learning Approaches for Diagnosing Building Systems

Enhanced Machine Learning Approaches for Diagnosing Building...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

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Noise influence on context formation and lexical retrieval in speech comprehension

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Journal of Neurolinguistics 2025年 76卷

作者： Cheng-Hung Hsin Chia-Ying Lee Taiwan International Graduate Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University and Academia Sinica Taipei Taiwan Brain and Language Laboratory Institute of Linguistics Academia Sinica Taipei Taiwan Biomedical Acoustic Signal Processing Lab Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Cognitive Neuroscience National Central University Taoyuan Taiwan Research Center for Mind Brain and Learning National Chengchi University Taipei Taiwan

Context-based lexical predictions facilitate speech comprehension, but noise can disrupt these mechanisms by degrading context formation and word retrieval. This study investigated how noise affects the word predictability effect during comprehension through analysis of three event-related potentials: The N200, N400, and late positive component (LPC). Sixty participants listened to clear and noise-masked sentences ending with high- or low-predictability words for comprehension across two experiments. In Experiment 1, we masked sentence frames to degrade context while preserving final words. In Experiment 2, we masked final words to hinder retrieval while preserving context. Clear speech in Experiment 1 elicited a typical N400 predictability effect. Importantly, noisy contexts elicited both a frontal N200 predictability effect and an unattenuated early N400 effect, suggesting that enhanced sound-meaning processing maintains comprehension despite continuous noise. In Experiment 2, both clear and noisy final words produced N400 and posterior LPC predictability effects, indicating that reanalysis helps consolidate word representations affected by transient and unpredictable sentence-end noise. These findings highlight how context formation and word retrieval jointly shape prediction under adverse listening conditions. The N200 and LPC effects reveal mechanisms of early phonological analysis and late reanalysis that support comprehension despite challenging acoustics. Our results illuminate the distinct cognitive mechanisms that support comprehension resilience through context use and word processing, advancing theories of speech comprehension.

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