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Neural Network System for Monitoring the Condition of Equipment and Predicting Malfunctions

Neural Network System for Monitoring the Condition of Equipm...

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2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Lazovskiy, Nikolay V. St. Petersburg Electrotechnical University 'LETI' Student of the Department of Robotics and Automation of Production Systems Saint Petersburg Russia

ISBN: (纸本)9798350360646

The scientific article 'Neural network system for monitoring equipment condition and fault prediction' explores an innovative approach to improving proactive maintenance of industrial equipment. The article presents a description of methods for continuous monitoring of the condition of machines and equipment. These methods allow you to collect a lot of data about the operation of equipment, and then use them to predict possible malfunctions. The neural network system contributes to more efficient maintenance management, reducing downtime and reducing repair costs. © 2024 IEEE.

关键词： condition monitoring data analysis data sensors equipment fault prediction industry maintenance neural network systems prediction accuracy production efficiency

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Neural Network System for Monitoring the Condition of Equipment and Predicting Malfunctions

Neural Network System for Monitoring the Condition of Equipm...

引用

IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Nikolay V. Lazovskiy Student of the Department of Robotics and Automation of Production Systems St. Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

The scientific article “Neural network system for monitoring equipment condition and fault prediction” explores an innovative approach to improving proactive maintenance of industrial equipment. The article presents a description of methods for continuous monitoring of the condition of machines and equipment. These methods allow you to collect a lot of data about the operation of equipment, and then use them to predict possible malfunctions. The neural network system contributes to more efficient maintenance management, reducing downtime and reducing repair costs.

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Extending Knowledge Representation System about Planners of Intelligent Information Agents

Extending Knowledge Representation System about Planners of ...

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2023 Seminar on Information systems Theory and Practice, ISTP 2023

作者： Ptitsyna, Larisa K. Ptitsyn, Nikita А. El Sabayar Shevchenko, Nidal Belov, Mikhail P. Management Information Systems Department The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Robotics and Automation in Production Systems Department Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9798350345193

The article presents the reasons for the relevance of extending knowledge about action planners of intelligent information agents. The key trends for the necessary extending in the context of the development of the digital economy are highlighted. The concept of forming a system for representing knowledge about action planners of intelligent information agents is proposed. The basic components of the quality profiles of the considered artifacts are described. Classes of models of action planners and methods for their analysis are defined. The formed components of the knowledge representation system about generic action planners of intelligent information agents are considered. ©2023 IEEE.

关键词： Knowledge representation

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Problems in Control systems of Electric Drives of Metalworking Machines

Problems in Control Systems of Electric Drives of Metalworki...

引用

2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Lazovskiy, Nikolay V. Kundyukov, Oleg A. Ershov, Kirill K. St. Petersburg State Electrotechnical University 'LETI' Department of Robotics and Automation of Production Systems Saint Petersburg Russia Petersburg State University of Industrial Technologies and Design Department of Automated Electric Drive and Electrical Engineering Saint Saint Petersburg Russia

The types of errors of machine tools are considered. Methods for increasing the productivity of machine tools and the accuracy of processing workpieces are analyzed. Promising directions for the development of electri... 详细信息

ISBN: (纸本)9798350360646

关键词： automation control systems electric drives metalworking machines

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Energy Management for Industrial Robots based on automationML

Energy Management for Industrial Robots based on AutomationM...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Valentyn Petrichenko Gregor Thiele Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Berlin Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Motivated by political regulations and rising energy prices, companies strive for implementing energy management and for reducing their energy consumption. It is an expenditure of work for the energy manager to gather all relevant information about the facilities and machines (e.g. meaning of measurement and control signals, limits of these quantities, installed nominal capacities). In many cases, there is digital planning data from the planning, construction and commissioning phases. Using automationML, this information can be used collaboratively not only for engineering and maintenance but for energy monitoring and optimization. In this study, an industrial robot and two conveyors are integrated in the wide-spread energy management software suite EnEffCo. We document the method and results. Implications for deriving optimization formulations are discussed.

关键词： Service robots Industrial robots Software Regulation Planning Maintenance Energy management Information technology Optimization Monitoring

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Research into the Influence of Informative Speech Parameters on the Reliability of RNN-based Classification of Human Emotional States 26

Research into the Influence of Informative Speech Parameters...

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Alimuradov, Alan K. Tychkov, Alexander Yu. Tyurin, Mikhail A. Dudnikov, Dmitry S. Tychkova, Anna N. Moiseev, Danil S. Myasnikova, Maria G. Student Research and Production Business Incubator Penza State University Penza Russia Penza State University Department of Radio Engineering and Radioelectronic Systems Penza Russia Penza State University Department of Automation and Telemechanics Penza Russia Penza State University Department of Scientific and Innovation Penza Russia Leninogorsk Oil College Department of Information Systems and Programming Leninogorsk Russia

ISBN: (纸本)9798350361728

Reliable classification of human emotional states from speech is dependent on speech signal processing algorithms and classification methods. The reliability is especially affected by the database of emotional speech informative parameters used to train classification methods. The paper is aimed at studying the influence of informative speech signal parameters on classification reliability of human emotional states based on a Recurrent Neural Network (RNN). The research is to expand the information space for emotional speech parameters through the use of an original signal representation algorithm based on Empirical Mode Decomposition (EMD), and assessing the impact on classification reliability using a five-layer RNN. The article outlines the research relevance and provides a brief overview of related academic papers. Local and global informative speech parameters relevant to human emotional states are considered in detail. The emotional speech database is described, and the speech signal representation algorithm based on modified decomposition methods is characterized. The research results on the method for classifying human emotional states based on a five-layer RNN using original and extended databases of emotional speech informative parameters are presented. Reliability assessment was performed by computing the confusion matrix for multi-class classification. The research results have evidenced an increase in classification reliability of human emotional states by an average of 2.66 % through expanding the information space for emotional speech parameters. The reliability ranges from 97.4 % to 98.3 % for the classification of emotional states, and from 95.8 % to 98.2 % for the classification of emotions. © 2024 IEEE.

关键词： Empirical mode decomposition

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RNN-Based Method for Classifying Natural Human Emotional States from Speech 25

RNN-Based Method for Classifying Natural Human Emotional Sta...

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25th International Conference on Digital Signal Processing and its Applications, DSPA 2023

作者： Alimuradov, Alan K. Tychkov, Alexander Yu. Dudnikov, Dmitry S. Tyurin, Mikhail A. Yuskaev, Marat I. Myasnikova, Maria G. Moiseev, Danil S. Student Research and Production Business Incubator Penza State University Penza Russia Penza State University Department of Radio Engineering and Radioelectronic Systems Penza Russia Penza State University Department of Automation and Telemechanics Penza Russia Leninogorsk Oil College Department of Information Systems and Programming Leninogorsk Russia

ISBN: (纸本)9798350337235

An accuracy of classifying human emotional states from speech has been dependent on emotional speech database, speech signal processing algorithms, and classification methods. Numerous static and dynamic classification techniques are used for emotional state recognition. The paper considers a novel method for classifying natural positive, negative, and neutral human emotional states based on a five-layer Recurrent Neural Network (RNN). The method novelty is due to various neuron activation functions employed for each network layer stemming from the peculiarities of natural emotional speech informative parameters used as input data for the neural network. Local and global informative speech parameters relevant to human emotional states are outlined, and the known classification methods are surveyed. The proposed classification method is described, and the developed Russian database of natural emotional speech is presented. The research results compared with the widely used classification methods have evidenced 95 % accuracy of the developed method, which can be effectively tested in systems for detecting and classifying natural human emotional states from speech. © 2023 IEEE.

关键词： Recurrent neural networks

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Learning to Segment Object Affordances on Synthetic Data for Task-oriented Robotic Handovers 33

Learning to Segment Object Affordances on Synthetic Data for...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Christensen, Albert Daugbjerg Lehotsky, Daniel Jørgensen, Marius Willemoes Chrysostomou, Dimitrios Department of Electronic Systems Aalborg University Fredrik Bajers Vej 7 Aalborg EastDK-9220 Denmark Robotics and Automation Group Department of Materials and Production Aalborg University Fibigerstraede 16 Aalborg EastDK-9220 Denmark

The ability to perform successful robot-to-human handovers has the potential to improve robot capabilities in the circumstances involving symbiotic human-robot collaboration. Recent computer vision research has shown that object affordance segmentation can be trained on large hand-labeled datasets and perform well in task-oriented grasping pipelines. However, producing and training in such datasets can be time-consuming and resource-intensive. In this paper, we eliminate the necessity for training in these datasets by proposing a novel approach in which training occurs on a synthetic dataset that accurately translates to real-world robotic manipulation scenarios. The synthetic training dataset contains 30245 RGB images with ground truth affordance masks and bounding boxes with class labels for each rendered object. The object set used for rendering consists of 19 object classes capturing 7 affordance classes. We propose a variant of AffordanceNet enhanced with domain randomization on the generated dataset to perform affordance segmentation without the need of fine-tuning on real-world data. Our approach outperforms the state-of-the-art method on synthetic data, by 23%, and achieves performance levels similar to other methods trained on massive, hand-labeled RGB datasets and fine-tuned on real images from the experimental setup. We demonstrate the effectiveness of our approach on a collaborative robot setup with an end-to-end robotic handover pipeline using various objects in real-world scenarios. Code, the synthetic training dataset, and supplementary material are publicly available at: https://***/AffNet-DR. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Pipelines

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Improved Contact Stability for Admittance Control of Industrial Robots with Inverse Model Compensation

Improved Contact Stability for Admittance Control of Industr...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Kangwagye Samuel Kevin Haninger Sami Haddadin Sehoon Oh Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University Munich Munich Germany Department of Mechanical and Production Engineering Kyambogo University Kampala Uganda Department of Automation Fraunhofer IPK Berlin Germany Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Industrial robots have increased payload, repeatability, and reach compared to collaborative robots, however, they have a fixed position controller and low intrinsic admittance. This makes realizing safe contact challenging due to large contact force overshoots in contact transitions and contact instability when the environment and robot dynamics are coupled. To improve safe contact on industrial robots, we propose an admittance controller with inverse model compensation, designed and implemented outside the position controller. By including both the inner loop and outer loop dynamics in its design, the proposed method achieves expanded admittance in terms of increasing both gain and cutoff frequency of the desired admittance. Results from theoretical analyses and experiments on a commercial industrial robot show that the proposed method improves rendering of the desired admittance while maintaining contact stability. We further validate this by conducting actual assembly tasks of plug insertion with fine positioning, switch insertion onto the rail, and colliding the robot end effector with random objects and surfaces, as seen at https://***/8XfkdHEdWDs.

关键词： Service robots Contacts Dynamics Cutoff frequency Observers Industrial robots Stability analysis Admittance Thermal stability Payloads

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automation of Life Data Analysis Processes 13

Automation of Life Data Analysis Processes

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13th International DAAAM Baltic Conference and 29th International Baltic Conference, BALTMATTRIB 2021

作者： Krämer, Jonathan Thiele, Gregor Johanni, Theresa Krüger, Jörg Department for Industrial automation The Institute for Machine Tools and Factory Management Technical University of Berlin Germany Department for Process automation and Robotics Fraunhofer Institute for Production Systems and Design Technology Germany

Life data analysis is an effective statistical process to gain information on the reliability of technical facilities. The Weibull analysis is a widely-used method for modeling the probabilities of different functional failures. For its application in modern manufacturing and maintenance processes, an automation of this analysis is expedient. An exemplary process of data collection and fitting of a parametric model with continuous time scale is analyzed in this article. Its automation with different statistic software tools is described and comparatively evaluated. With the ambition of minimizing manual effort in the process, a highly automated solution was developed in this research. We describe this self-implemented solution in "R" and justify that this procedure automatizes the process more than the examined commercial implementations. The here presented work proves to be a practical real-time assistance, right now applied in aircraft maintenance. © 2021 DAAAM International. All rights reserved.

关键词： automation

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