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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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A study of vibration and current data characteristic analysis for motor mechanical fault level determination by deep learning

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Transactions of the Korean Institute of Electrical Engineers 2021年第10期70卷 1509-1516页

作者： Han, Ji-Hoon Park, Sang-Uk 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 classic motor fault diagnosis system, a method of analyzing the differences between the normal and collected state signals of the motor to be diagnosed was used and the method can diagnose only the limited situations because the diagnosis is based on the frequency of the mechanical failure. In order to compensate for this, some studies on a system that performs more specialized fault diagnosis through deep learning algorithms were carried out. However, the level of failure cannot be determined because these studies consider only the signals that have a great influence on motor operation. To solve this problem, the characteristics of vibration and current signals are analyzed to develop a deep learning algorithm suitable for fault level determination. The characterized signals are used for fault diagnosis and prediction. Fault diagnosis based on vibration signal is carried out through DT-CNN (Decision Tree Convolutional Neural Network). In addition, it is checked whether the current signal in the initial failure state, which is relatively insensitive to failure, can be classified through a deep learning algorithm. The proposed data utilization performance was evaluated through an induction motor and the analyzed signal-based fault diagnosis system is expected to enable a more precise diagnosis compared to the existing system. Copyright © The Korean Institute of Electrical Engineers.

关键词： Failure analysis

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A Probabilistic Topology Inference Method for Networked Dynamical system via Single Excitation 63

A Probabilistic Topology Inference Method for Networked Dyna...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Jiao, Qing Li, Yushan He, Jianping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Dept. of Automation Shanghai China

ISBN: (纸本)9798350316339

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Network topology

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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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A Study On Suitable Electrodes For Low-Energy Power Generation From Carbon Waste in Factories

A Study On Suitable Electrodes For Low-Energy Power Generati...

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2023 International Electrical engineering Congress, iEECON 2023

作者： Channgamthiang, Surasak Nusukhon, Pheeraphat Chansri, Pakpoom King Mongkut's University of Technology Thonburi Dept. Electrical Technology Education Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. Control System and Instrumentation Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

Factory waste includes the incomplete combustion of industrial fuels that causes carbon to precipitate in vents. These carbons might be put to good use. Therefore, the impact of using waste carbon in factories to produce electricity is investigated in this study by evaluating electrical characteristics: the type of electrode material's suitability, the size of the electrode's carbon contact surface area, and the distance between the electrodes. According to the findings, the maximum open circuit voltage (Voc) was 1.09 V for copper (+) and zinc (-) electrode materials, 1.11 V for the electrode's carbon contact surface area, and 1.29 V for the electrodes' spacing. The maximum of Voc was 1.11 V, maximum on-load voltage and current were 0.64V, 124.65 mA for optimum electrode carbon contact surface size 40 cm2. Optimum electrode distance at 2 cm that maximum open circuit voltage (Voc) was 1.29 V. The maximum load voltage and current were 0.77V, 128.32mA. Factory waste carbon can be useful in some applications for producing electricity from waste carbon and assisting in the reduction of factory waste. © 2023 IEEE.

关键词： Carbon

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