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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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Solution to Mitigate the Disadvantages in the Design of LLC Resonant Converters for Electric Vehicle Fast Chargers with High Output Power and Wide Output Voltage Range

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 316-325页

作者： Park, Ha-Min Lee, Hyung-Woo Park, Chan-Bae Guo, Youguang Lee, Jae-Bum Dept. of Railway Vehicle & Operation System Engineering Korea National University of Transportation Korea Republic of Dept. of Electrical and Data Engineering University of Technology Sydney Australia Dept. of Railroad Electrical and Information Engineering Korea National University of Transportation Korea Republic of

As the number of electric vehicles grows, the development of fast chargers is advancing rapidly. A DC-DC converter that can deliver high output power, a fixed input voltage, and a wide output voltage range is critical for electric vehicle fast chargers. While the LLC resonant converter is commonly used in these converters, it requires a low magnetizing inductance, which increases the transformer air gap, causing additional AC losses in the windings due to the fringing effect. To mitigate these issues, this study explores design solutions in LLC resonant converters. By using two transformers per module, with the primary sides connected in series and the secondary sides in parallel, the number of primary turns can be reduced, minimizing the air gap. This reduction also allows for structural changes in the windings, improving heat dissipation. Magnetic field analysis and circuit simulations were conducted to validate the proposed DC-DC converter design. Copyright © The Korean Institute of Electrical Engineers.

关键词： Transformer windings

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Augmented Reality Used in Technical Diagnostics and Maintenance 15

Augmented Reality Used in Technical Diagnostics and Maintena...

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15th International Conference Elektro, ELEKTRO 2024

作者： Janota, Ales Holecko, Peter Faculty of Electrical Engineering and IT UNIZA Dept. of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350372359

Integrating digital data and human sensory input in real-world settings offers new possibilities in device diagnostics and maintenance. In this study, we utilized the EcoStruxure Augmented Operator Advisor to develop an augmented reality application for a family house heating system. Our application was designed without access to the real heating system;instead, we simulated its operation with a programmable logic controller. Our final application includes three primary scenes: the heat pump, its indoor unit, and the engine room. Drawing on our practical experience, we discuss the benefits of our approach and the software tools used and summarize our subjective opinions. © 2024 IEEE.

关键词： Maintenance

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Automatic Traffic Monitoring using Spatial Transformer Network and Monocular dept. Estimation 2

Automatic Traffic Monitoring using Spatial Transformer Netwo...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Kurti, Adam Szemenyei, Marton Budapest University of Technology and Economics Dept. of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9798350307733

Automatic license plate recognition and vehicle speed measurement are two of the critical tasks in traffic monitoring that face several challenges. Existing CNN and RNN-based license plate recognition systems must manage significantly distorted input images or possess complex architecture leading to slower training and inference times. Speed measurement performed with dedicated hardware like various lasers, and Doppler radars have significant acquisition costs, while existing single camera solutions that use the geometric information of the road or homographic mapping have often cumbersome calibration methods and may not always provide accurate results. This paper proposes a license plate recognition solution based on a fully convolutional deep neural network combined with a spatial transformer network, which enables segmentation-free recognition of license plates while addressing the spatial weaknesses of convolutional networks. Additionally, a monocular camera-based speed measurement solution is proposed that builds on metric dept. estimation algorithms and uses 3D reconstruction to estimate vehicle speed. The license plate recognition results show competitive accuracy and inference speed compared to other existing solutions while the proposed speed estimation method shows promising potential as an easy to calibrate and cost-effective solution. © 2024 IEEE.

关键词： Deep neural networks

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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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Multi-Objective Cooperative control of Vehicle Platoon Based on Fuzzy MPC 26

Multi-Objective Cooperative Control of Vehicle Platoon Based...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhu, Wanying Yin, Yunfei Xu, Zhanguo Wei, Yuanlong Zeng, Shenjun Harbin Institute of Technology Dept. of Traffic Information and Control Engineering Harbin China Jilin University Dept. of Traffic Information and Control Engineering Changchun China

ISBN: (纸本)9798331521950

To simultaneously optimize multiple performances of the cooperative adaptive cruise control (CACC) system, a fuzzy MPC control strategy is proposed to achieve multi-objective cooperative control for electric vehicle platoons. The longitudinal kinematic analysis of the vehicle platoon is conducted to establish the platoon model. Based on the analysis of system performance, safety, comfort, and following performance are selected as the system's performance indexes, and a comprehensive performance function for the MPC is constructed. Fuzzy control theory is employed to optimize the weight parameters, enabling the CACC system to adapt to different conditions. The effectiveness and reliability of the fuzzy MPC control strategy are verified through co-simulation using Carsim and Simulink. © 2025 IEEE.

关键词： control system analysis

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention 2

Big Data Analytics for Proactive Financial Fraud Detection a...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Hasudungan, Yehezkeil Oganda, Fitra Putri Daeli, Ora Plane Maria Raihan Santoso, Nesti Anggraini University of Raharja Dept. of Informatics Engineering Tangerang Indonesia University of Raharja Dept. of Information System Tangerang Indonesia University of Raharja Dept. of Digital Business Tangerang Indonesia University of Raharja Dept. of Computer System Tangerang Indonesia

ISBN: (纸本)9798350351613

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics. © 2024 IEEE.

关键词： Data Analytics Financial Fraud Prevention Proactive Detection Risk Management SmartPLS

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The Transient Predictor 63

The Transient Predictor

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Moffat, Keith Dorfler, Florian Chiuso, Alessandro Automatic Control Laboratory Zürich Switzerland University of Padova Dept. of Information Engineering Italy

ISBN: (纸本)9798350316339

This paper introduces the Transient Predictor and describes how it can be used to estimate the Multistep Predictor, which can be applied to applications such as Data-Driven Predictive control (DDPC). The Transient Predictor has two desirable traits that differentiate it from other methods for estimating the Multistep Predictor, such as the standard Subspace Predictor method: 1) Causality-the Transient Predictor asserts a causal relationship between future inputs and future outputs;and 2) Bias-the Transient Predictor is a consistent predictor of future outputs. This paper provides an easy-toimplement algorithm for estimating the Transient Predictor and in turn the Multistep Predictor, and demonstrates its efficacy for DDPC. In experiments, we find that the Transient Predictorbased DDPC performs remarkably well with small lead-in data lengths, indicating that it is well-suited for tasks in which large amounts of data are not available. In addition, the Transient Predictor is not afflicted by the same bias as subspace-based methods when data is gathered in closed loop. © 2024 IEEE.

关键词： Electromagnetic transients

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Unraveling Genetic Complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size 5

Unraveling Genetic Complexity: Fine-tuning Machine Learning ...

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Lukman Adnyana, I Made Anggar Dwi Purnama, Fajar Latumakulita, Luther Alexander Rosalia, Ayang Armelita Pawana, I Wayan Adi Juliawan Indonesia University of Education Dept. of Marine Information System Bandung Indonesia Udayana University Dept. of Electrical Engineering Denpasar Indonesia Udayana University Dept. of Information Technology Denpasar Indonesia Sam Ratulangi University Dept. of Information System Manado Indonesia

ISBN: (纸本)9798350369489

In the realm of genetic analysis, the utilization of machine learning models holds great potential for unraveling the intricate complexities of DNA sequence data. This study aims to address the pressing need for advanced DNA sequence analysis methods by leveraging K-mer representations and fine-tuning machine learning algorithms to achieve optimal performance. By elucidating the reasons behind the study and highlighting the significance of these approaches, this research contributes to the broader understanding of DNA sequence intricacies. © 2023 IEEE.

关键词： DNA sequences

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