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Method of Classifying Motor Failure Conditions Using an Auto-Encoder-Based Ensemble Technique

Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 273-279页

作者： Choi, Eui-Jin Han, Ji-Hoon Song, Seung-Min Park, Jong-Hoon Hong, Sun-Ki Dept. of System and Control Engineering Hoseo University Korea Republic of Dept. of Information Control Engineering Hoseo University Korea Republic of

In the field, unsupervised learning-based fault diagnosis is necessary due to the lack of fault data. However, conventional auto-encoder models face challenges in fault classification. To address this issue, this study proposes an unsupervised learning-based auto-encoder model enhanced with an ensemble approach. The proposed model performs fault classification using gear, bearing, and eccentricity fault data, and employs the Layer-wise Relevance Propagation (LRP) technique to improve fault classification. Through the LRP technique, the key frequency bands relevant to each fault are identified and incorporated into the learning process, allowing for frequency band-based training. As a result, the proposed auto-encoder model improves classification performance and effectively distinguishes between different types of faults. © The Korean Institute of Electrical Engineers.

关键词： Unsupervised learning

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Study on Improving Hysteresis Motor Efficiency by Over-Excitation Based on Finite Element analysis

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 49-55页

作者： Park, Jong-Hoon Han, Ji-Hoon Choi, Eui-Jin Hong, Sun-Ki Dept. of Information Control Engineering Hoseo University Korea Republic of Dept. of System and Control Engineering Hoseo University Korea Republic of

A hysteresis motor is a motor characterized by an increase in the efficiency of the output to input ratio during an over-excitation in which a higher voltage is applied at startup than when the rated voltage is applied, and the voltage is reduced over a period of time after entering synchronous speed. However, due to the specificity of the rotor material, finite element analysis of hysteresis motors is difficult, so the design was conducted by analyzing only the rated voltage application condition through an equivalent circuit. As a result, the improved efficiency and torque that can be achieved by over-excitation were not verified, which was confirmed by experiments after the design was completed. However, recently, finite element analysis simulation that can express the hysteresis loop behavior has become possible, so finite element analysis of hysteresis motors has become possible, and the improved efficiency and torque that can be obtained through over-excitation can be confirmed. Therefore, this paper compares the output and efficiency of hysteresis motors under over-excitation and rated voltage application, and confirms the necessity and feasibility of designing hysteresis motors considering over-excitation. Copyright © The Korean Institute of Electrical Engineers.

关键词： Equivalent circuits

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Performance Improvement of Transfer Learning-based Motor Fault Diagnosis Using Mix-up Data Considering Data Imbalance

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Transactions of the Korean Institute of Electrical Engineers 2023年第2期72卷 255-262页

作者： Choi, Eui-Jin Han, Ji-Hoon Hong, Sun-Ki Dept. of System and Control Engineering Hoseo University Korea Republic of Dept. of Information Control Engineering Hoseo University Korea Republic of

Most of the data obtained for motor fault diagnosis is the data of normal motors. Therefore, the model is trained using normal data collected in the field and fault data collected in experiment. In addtion, transfer learning is used for fast learning convergence. Fault diagnosis performance may be reduced by the number of unbalanced data in each state during transfer learning. In this study, a transfer learning method using mix-up data is proposed to solve this data imbalance. A mix-up is a technique of adding insufficient data to the data with clear decision boundaries, and can be used to balance a large number of normal state data with a small number of fault-state data. The proposed method is compared with GAN, another technique of data augmentation. As a result of the experiment, it was confirmed that the proposed mix-up technique is a method to improve the performance of fault diagnosis. Copyright © The Korean Institute of Electrical Engineers.

关键词： Failure analysis

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Performance analysis of Transfer-Learning Based Physics-Informed Neural Network for Effective Shape Variation Adaptation with Varying Hyper-parameters

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Transactions of the Korean Institute of Electrical Engineers 2023年第10期72卷 1149-1158页

作者： Han, Ji-Hoon Choi, Eui-Jin Hong, Sun-Ki Dept. of System and Control Engineering Hoseo University Korea Republic of Dept. of Information Control Engineering Hoseo University Korea Republic of

One of the most time-consuming parts of designing an electrical device is optimizing the geometry. Optimization involves fine-tuning dimensions such as slot widths and airgap lengths from a base geometry to meet user needs. FEA(Finite Element analysis) is typically used to verify this performance. However, conventional FEA requires a separate analysis if the analysis geometry is varied even slightly. This causes a very long time consumption, and a transfer learning-based PINN(Physics-Informed Neural Network) is proposed to solve this problem. Since PINNs are at the basic level worldwide, their performance is analyzed according to hyper-parameters such as model parameters (e.g. weights or biases), preprocessing methods, activation functions, and the number of training data to increase their performance. Based on the analyzed hyper-parameters, the performance of the transfer learning-based PINN is verified under the condition of varying the airgap length of the E-I core. © 2023 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Geometry

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Performance Evaluation of the Continuos Wavelt Transformation Data in Motor Fault Diagnosis through XAI Algorithm

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 225-232页

作者： Han, Ji-Hoon Park, Sang-Uk Hong, Sun-Ki Dept. of Information Control Engineering Hoseo University Korea Republic of Dept. of System and Control Engineering Hoseo University Korea Republic of

The data mainly used for motor fault diagnosis is FFT. However, preprocessing such as continuous discrete wavelet transform is used. When using deep learning algorithms, the performance of the data is evaluated by the model output. However, in order to improve the learning possibilities and preprocessing performance of data, performance evaluation from a model perspective is required. For this purpose, data performance evaluation using the LRP algorithm, one of the XAI techniques, is studied. Initial fault state diagnosis using current data, which is difficult to solve with FFT data, is performed with STFT and CWT data, and performance is evaluated through LRP. Experimental Results STFT and CWT are preprocessing techniques that enable the use of current signals for early fault diagnosis. Among the two preprocessing methods, the use of CWT is more preferable because the flexibility of the preprocessing is increased. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Failure analysis

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The Method of for Analyzing EMI Conducted Noise analysis in Power Conversion Devices using Partial Element Equivalent Circuit Modeling

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Transactions of the Korean Institute of Electrical Engineers 2023年第7期72卷 809-819页

作者： Seo, Hyun-Woo Kim, Rae-Young Dept. of Electrical Engineering Hanyang University Korea Republic of Dept. of Energy Power Electronics Control System Lab Hanyang University Korea Republic of

EMI conducted noise is becoming increasingly important in power conversion systems, and various analysis methods are being researched. The proposed method enables accurate EMI conducted noise analysis by extracting partial element equivalent circuits from the 3D structure of power cables and PCBs, and the extracted model can be interpreted in SPICE simulations. The implemented inverter operation mode of Totem-Pole bridge-less PFC in the EMI conducted noise analysis simulation model shows a good correlation between simulation results and experimental results in all CE bands. Furthermore, this method identifies the main causes of EMI noise through CM (Common Mode) and DM (Differential Mode) noise analysis, and proposes a method to effectively attenuate EMI noise through CM coil modeling. Therefore, the EMI conduction noise analysis method using simulation models allows for efficient prediction of EMI conduction noise in advance. Copyright The Korean Institute of Electrical Engineers.

关键词： Poles

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control of Deterministic Chaotic Modes in Power systems

Control of Deterministic Chaotic Modes in Power Systems

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2023 IEEE International Conference on Smart Information systems and Technologies, SIST 2023

作者： Beisenbi, Mamyrbek Satpayeva, Ainur Mussabayeva, Shugla Mussina, Gulnara Tlegen, Meruert L.N. Gumilyov Eurasian National University Dept. of System Analysis and Control Astana Kazakhstan Astana It University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

The paper examines a new approach to controlling deterministic dynamical chaotic mode of power system with two electric generate-sources. A control law is synthesized in the class of one-parameter structurally stable mappings from catastrophe theory. Analytical calculations and a numerical experiment in MatLab showed that the proposed system excludes the generation of a deterministic chaotic mode for any changes in the uncertain system parameters. The study of the control system is carried out by the gradient-velocity method Lyapunov vector functions © 2023 IEEE.

关键词： Uncertainty analysis

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Blockchain Based Digital Identity Management system for Smart City Services 5

Blockchain Based Digital Identity Management System for Smar...

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5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Varfolomeev, Alexander A. Al-Farhani, Liwa H. Bmstu Nrnu Mephi Rudn University Dept.Information Security Moscow Russia Rudn University Academy Of Engineering Dept. System Analysis Control And Information Processing Moscow Russia

ISBN: (纸本)9798350335118

Centralized systems that require third parties remain opaque and represent a single point of failure. Therefore, in accordance with the requirements of cloud services and identity management mechanisms, it is necessary to move towards more transparent and collaborative methods. That is, all service providers under the same federation deal positively with the issued identity of the user. The distributed format gives good potential for ID management systems and treats everyone with the same level of validity. This paper presents a blockchain-based identity management system for the purpose of creating a federated identity management system that is used by multiple organizations by Taking advantage of decentralization and high transparency in the blockchain. Several vital advantages of blockchain are positive for forming identity management systems characterized by transparency and high security, and privacy. Implementing the proposed system on a private Hyperledger Fabric blockchain network proved effective according to the criteria studied, and there is also great potential for improvement. The practical application of the proposed system proved high quality and promising possibilities in blockchain based identity management. © 2023 IEEE.

关键词： Blockchain

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Methodology for Creating Annotated Datasets of Biological Objects in Microscopic Images 5

Methodology for Creating Annotated Datasets of Biological Ob...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Kovalenko, Sergii Kovalenko, Svitlana Kutsenko, Alexander Godlevskyi, Mykhailo Severin, Valery Kovalenko, Anton Dept. of System Analysis and Information-Analytical Technologies National Technical University "KhPI" Kharkiv Ukraine Dept. of Software Engineering and Management Intelligent Technologies National Technical University "KhPI" Kharkiv Ukraine

ISBN: (纸本)9798350384499

The study presents a methodology for automatically creating an annotated dataset for further use in training neural networks. The method is based on the Crisp-DM framework and consists of 6 stages: business understanding, data understanding, data preparation, modeling, evaluation, deployment, which has been modified due to the specifics of computer vision tasks. As a modeling method, the k-means method is proposed, which is applied to the normalized area and average color data of a given object. The approach is tested for creating an annotated dataset for recognizing and localizing cells of different types in blood smear images. The output is a dataframe with the columns file_name, xmin, ymin, xmax, ymax, type, area, which represent the file name, the coordinates of the upper left and lower right points of the bounding boxes, the cell type, and its area. Using these parameters and image processing libraries it is easy to add bounding boxes to the original images. The resulting annotated dataset is analyzed. Metrics were calculated and a loss matrix was constructed. All calculations were performed using the Python programming language and the Pandas, numpy, openCV, and matplotlib libraries. ©2024 IEEE.

关键词： Image segmentation

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Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Meteorology Predicting Technology Based on Artificial Intell...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Abitova, Gulnara Mukashev, Daniyar Aitmukhanbetova, Elvira Rakhimzhanova, Mira Uskenbayeva, Gulzhan Kulniyazova, Korlan Binghamton University Dept Electrical Engineering and Computer Science Binghamton United States Astana It University Dept. of Computer Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Dept. System Analysis and Management Astana Kazakhstan

ISBN: (纸本)9798350372229

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage. © 2024 IEEE.

关键词： artificial intelligence data analysis machine learning neural networks predictive analytics weather Forecasting

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