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11th IEEE International Conference on E-Learning in Industrial Electronics, ICELIE 2024

作者： Gaweda, Bartlomiej Bauer, Waldemar Baranowski, Jerzy AGH University of Krakow Dept. of Automatic Control and Robotics Krakow Poland

ISBN: (纸本)9798350363654

In this paper we consider an important problem in teaching of advanced theory is that you need access to complicated software. In many cases in order to allow students to participate in more challenging classes, like Bayesian data analysis, or advanced FPGA programming you encounter an issue that setting up the relevant software on their machines consumes significant part of the course, and you need to troubleshoot issues that are irrelevant to the actual topic. We present a solution from our own experience that is using container software i.e. Docker and we show how it significantly improved our work when teaching data analytics and Bayesian methods in general. © 2024 IEEE.

关键词： containers probabilistic programming teaching statistical modeling

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Mixture Based Classifier Using Gaussian Processes for Induction Motor Diagnosis 50

Mixture Based Classifier Using Gaussian Processes for Induct...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Dudek, Adrian Jarzyna, Kacper Baranowski, Jerzy Dept. of Automatic Control and Robotics AGH University of Kraków Kraków Poland

ISBN: (纸本)9781665464543

This paper explores the use of Gaussian Processes (GPs) and Gaussian Mixture Models (GMMs) for diagnosing induction motors. GPs provide flexible, non-linear models that handle noisy data, while GMMs offer robust probabilistic classification by modeling data as mixtures of Gaussian distributions. By integrating Bayesian inference with GMMs and utilizing Stan for complex model management, the study enhances classification accuracy. Experimental data from induction motors under various conditions were analyzed, identifying patterns indicative of motor health. The approach, leveraging synthetic data generation, demonstrates effectiveness in proactive maintenance and fault detection, reducing downtime and costs. © 2024 IEEE.

关键词： Global positioning system

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks 50

Detection of Electric Motor Damage Through Analysis of Sound...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bauer, Waldemar Zagórowska, Marta Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9781665464543

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided. © 2024 IEEE.

关键词： Damage detection

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Deep of Recurrent Neural Networks and Bayesian Neural Networks for Detecting Electric Motor Damage Through Sound Signal Analysis

arXiv

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arXiv 2025年

作者： Bauer, Waldemar Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Poland

Fault detection in electric motors is a critical challenge in various industries, where failures can result in significant operational disruptions. This study investigates the use of Recurrent Neural Networks (RNNs) and Bayesian Neural Networks (BNNs) for diagnosing motor damage using acoustic signal analysis. A novel approach is proposed, leveraging frequency domain representation of sound signals for enhanced diagnostic accuracy. The architectures of both RNNs and BNNs are designed and evaluated on real-world acoustic data collected from household appliances using smartphones. Experimental results demonstrate that BNNs provide superior fault detection performance, particularly for imbalanced datasets, offering more robust and interpretable predictions compared to traditional methods. The findings suggest that BNNs, with their ability to incorporate uncertainty, are well-suited for industrial diagnostic applications. Further analysis and benchmarks are suggested to explore resource efficiency and classification capabilities of these architectures. © 2025, CC BY.

关键词： Recurrent neural networks

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PDDL optimal planning as the Linear Programming Problem in a seaport container terminal

PDDL optimal planning as the Linear Programming Problem in a...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Daniel Wójtowicz Krzysztof Puszyński Adam Gałuszka Dept.: Automatic Control and Robotics Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

International Seaports, as in the Shanghai International Container Seaport example, have problem with Planning Seaport Operations and one of the approaches to solve this planning problem is Seaport Operations Planning in the Planning Domain Description Language (PDDL). The problem with the PDDL planning is that it is computational resource intensive. That is why we have introduced transformation from the PDDL planning to linear programming in order to solve it. Linear programming is computational resource light. The plan computed in this way requires binary decision on an action. Therefore the nonbinary decision of linear programming algorithm computation has been interpreted as degrees of truth.

关键词： Integer programming Automation Computational modeling Seaports Containers Linear programming Planning

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Using Docker Containers in Teaching of Advanced Data Analytics Methods

Using Docker Containers in Teaching of Advanced Data Analyti...

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IEEE International Conference on E-Learning in Industrial Electronics, ICELIE

作者： Bartłomiej Gawęda Waldemar Bauer Jerzy Baranowski Dept. of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350363654

ISBN: (纸本)9798350363661

关键词： Industrial electronics Data analysis Electronic learning Education Containers Programming Probabilistic logic Software Bayes methods Field programmable gate arrays

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Design of Output and Impacts control Systems

Design of Output and Impacts Control Systems

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Gaiduk, Anatoly Prokopenko, Nikolay Chumakov, Vladislav Southern Federal University Scientific Design Office Robotics and Control Systems Taganrog Dept. of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Dept. of Scientific Research Dept. of Information Systems and Radio Engineering Rostov-on-Don Russia

ISBN: (纸本)9798350345551

To design linear control systems a new method is suggested on the basis of the principle of output and impacts control. The main difference of this principle consists in the use of only measurable variables and separate operators for each input signal of the control device. The design problem has a solution if the plant is completely controllable, and at least the controllable variable is measured. The suggested approach makes it possible to eliminate the well-known contradiction between the accuracy and stability of systems with control on deviation, and to provide the required astatism orders or selective invariance to the external actions. At the same time, the conditions of ensuring the direct indicators of the transient quality and the physical implementation of the control device are taken into account. The coefficients of the equation of the output and impacts control device are determined by solving a system of linear algebraic equations. The control device can be implemented on the basis of either operational amplifiers or sufficiently high-speed digital automation tools. The suggested method can be applied for designing high-quality linear control systems for various purposes. © 2023 IEEE.

关键词： Operational amplifiers

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Mixture Based Classifier Using Gaussian Processes for Induction Motor Diagnosis

Mixture Based Classifier Using Gaussian Processes for Induct...

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Annual Conference of Industrial Electronics Society

作者： Adrian Dudek Kacper Jarzyna Jerzy Baranowski Dept. of Automatic Control and Robotics AGH University of Kraków Kraków Poland

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

关键词： Induction motors Uncertainty Systematics Mixture models Predictive models Probabilistic logic Data models Maintenance Noise measurement Synthetic data

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

Detection of Electric Motor Damage Through Analysis of Sound...

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Annual Conference of Industrial Electronics Society

作者： Waldemar Bauer Marta Zagórowska Jerzy Baranowski Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

关键词： Accuracy Fault detection Neural networks Training data Robustness Bayes methods Wind turbines Signal representation Time-domain analysis Electric motors

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

arXiv

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arXiv 2024年

关键词： Electric motors

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