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Reviewing Fault Diagnosis methods in Electric Drives: Power Subsystem and Electrical Machine 24

Reviewing Fault Diagnosis Methods in Electric Drives: Power ...

引用

24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Demidova, Galina Chen, Hao Li, Yibin Lukichev, Dmitry Anuchin, Alecksey Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Moscow Power Engineering Institute Department of Electric Drives Moscow Russia

ISBN: (纸本)9798350336870

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives. © 2023 IEEE.

关键词： Failure analysis

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Improving Object Distance Measurement Based on Mono Camera Using Magnification and Yolo methods

Improving Object Distance Measurement Based on Mono Camera U...

引用

Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Herdianto Herdianto Poltak Sihombing Fahmi Fahmi Tulus Tulus Computer Science Universitas Sumatera Utara Medan Indonesia Electrical Engineering Universitas Sumatera Utara Medan Indonesia Mathematics Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

In the Autonomous Driver System (DAS), object detection and distance measurement are among the parameters that are consistently monitored for changes. This is done to modify the action that will be determined, whether to turn left to right or halt in order to prevent a collision. The issue with utilizing a mono camera to measure the distance of objects is that the utmost distance for objects that are measured by humans is only 7 meters. For this reason, the objective of this investigation is to determine the distances to numerous objects using a mono camera, with a measured distance exceeding 15 meters. Magnification and You Only Look Once (YOLO) are employed. The results of this investigation are quantifiable objects that have been tested, such as humans, bottles, and jerry cans. For humans, the measured distance to objects is 31.40 meters, with a body height of 1.75 meters. For bottles, the measured object distance is up to 4.80 meters with an object height of 0.26 meters, while for jerry cans, the measured object is 4.20 meters with an object height of 0.17 meters.

关键词： Meters YOLO Robot vision systems Estimation Cameras Distance measurement MONOS devices Internet of Things Robots Monitoring

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Fast and Robust Normal Estimation for Sparse LiDAR Scans

Fast and Robust Normal Estimation for Sparse LiDAR Scans

引用

IEEE international conference on robotics and automation (ICRA)

作者： Igor Bogoslavskyi Konstantinos Zampogiannis Raymond Phan Magic Leap

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today’s mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses *** this paper, we address the problem of estimating normals for sparse LiDAR data avoiding the typical issues of smoothing out the normals in high curvature *** LiDARs rotate a set of rigidly mounted lasers. One firing of such a set of lasers produces an array of points where each point’s neighbor is known due to the known firing pattern of the scanner. We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them. When estimating normals at these points, we only consider points with the same label as neighbors. This allows us to avoid estimating normals in high curvature *** evaluate our approach on various data, both self-recorded and publicly available, acquired using various sparse LiDAR sensors. We show that using our method for normal estimation leads to normals that are more robust in areas with high curvature which leads to maps of higher quality. We also show that our method only incurs a constant factor runtime overhead with respect to a lightweight baseline normal estimation procedure and is therefore suited for operation in computationally demanding environments.

关键词： Mechanical sensors Laser radar Runtime Smoothing methods Firing Lasers Estimation

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Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots

Describing Robots from Design to Learning: Towards an Intera...

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international conference on Advanced robotics and Mechatronics (ICARM)

作者： Nuofan Qiu Chaoyang Song Fang Wan School of Design Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The robot development process is divided into several stages, which create barriers to the exchange of information between these different stages. We advocate for an interactive lifecycle representation, extending from robot morphology design to learning, and introduce the role of robot description formats in facilitating information transfer throughout this pipeline. We analyzed the relationship between design and simulation, enabling us to employ robot process automation methods for transferring information from the design phase to the learning phase in simulation. As part of this effort, we have developed an open-source plugin called ACDC4Robot for Fusion 360, which automates this process and transforms CAD software into a user-friendly graphical interface for creating and editing robot description formats to solve the Design2Sim problem. Additionally, we offer an out-of-the-box robot model library to streamline and reduce repetitive tasks. All codes are hosted open-source. (https://***/bionicdl-sustech/ACDC4Robot)

关键词： Solid modeling Mechatronics Codes automation Pipelines Morphology Transforms Software Libraries Robots

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Knowledge Graph Construction: State-of-the Art Techniques and Future Opportunities

Knowledge Graph Construction: State-of-the Art Techniques an...

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Information Systems and Computing Technology (ISCTech), international conference on

作者： Qingwang Wang Chaohui Li Yinxing Zhang Yi Liu Hao Xiong Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Knowledge graphs represent structured mappings of human knowledge playing a pivotal role in the iterative development and deepening of domain understandings. methods for knowledge graph construction are continually evolving. Advancements in artificial intelligence, particularly with the advent of Generative Pre-trained Transformers (GPT), have paved the way for end-to-end automated methods for constructing knowledge graphs. This study provides a comprehensive evaluation of three conventional methodologies for constructing knowledge graphs: Top-Down, Bottom-Up and Joint approaches. We survey the associated technologies and present classic knowledge graph cases. We then elucidate the integration of large language models into the automation of knowledge graph construction, including their functions, benefits and limitations. Finally, we offer a prospective outlook on future research trajectories in this field.

关键词： Training Surveys Adaptation models automation Large language models Computational modeling Knowledge graphs Transformers Trajectory Security

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A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

A Traffic Model Integrating Long Short-Term Memory Networks ...

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IEEE international conference on automation Science and Engineering (CASE)

作者： K. Binjaku C. Pasquale S. Sacone E. K. Meçe Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on/off ramps, to enhance traffic state prediction accuracy. We develop both univariate and multivariate versions of the physics-informed multi-class LSTM model, enabling comprehensive exploration of traffic variables such as speed and flow for passenger and heavy vehicles. Experimental results demonstrate the superior predictive capabilities of our approach compared to traditional LSTM models, particularly in capturing the intricate relationship between speed and flow variables. Our findings underscore the significant benefits of incorporating physics-based principles into deep learning architectures for traffic prediction, facilitating more robust and reliable traffic state estimations for real-world applications.

关键词： Accuracy Urban planning Transportation Predictive models Reliability engineering Mathematical models Robustness State estimation Vehicle dynamics Long short term memory

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Research on Unsupervised Fault Diagnosis based on Sparse Autoencoder and Locally Linear Embedding

Research on Unsupervised Fault Diagnosis based on Sparse Aut...

引用

robotics, Artificial Intelligence and Intelligent Control (RAIIC), international conference on

作者： Yichen Li Guangye Liu Wei Zeng Lei Jiang Hubei Xinye Reconstituted Tobacco Development Co. Ltd. China Tobacco Hubei Industrial Co. Ltd. Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

In this paper, we address the shortcomings of traditional unsupervised fault diagnosis methods in handling high-dimensional, nonlinear, and highly complex fault diagnosis problems, and propose an unsupervised fault diagnosis method based on sparse autoencoder and local linear embedding. This method combines the characteristics of sparse autoencoder and manifold learning methods to reduce dimensionality and extract features from high-dimensional data while preserving the local linear relationships within the data. The proposed model was experimentally validated on a three-phase flow equipment dataset, demonstrating the effectiveness of the model.

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Verification of Compliance on Funabot-Plate: Semisoft Assistive Robot with Artificial Muscle and Rigid Plate

Verification of Compliance on Funabot-Plate: Semisoft Assist...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Shinichi Masaoka Rin Yasuda Yuki Funabora Shinji Doki Graduate School of Engineering Nagoya University Aichi Japan

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Compliance is important for the rehabilitation of wearable assistive robots. Various methods have been studied to improve the compliance of wearable robots, such that the use of impedance control of actuators and soft actuators with high compliance. On the other hand, for robots whose degrees of freedom of movement are limited by rotational axes like conventional robots, compliance performance may deteriorate due to mounting misalignment even if the actuator is designed optimally. Soft wearable devices that do not have rigid joints and are composed of soft base materials also exist, but the force is dispersed and cannot exert great force. Our Funabot- Plate consists of a base cloth, McKibben-type pneumatic artificial muscles, and thin, rigid plates that efficiently transfer the exerted force of the artificial muscles to the human body. Thus, Funabot-Plate expects a high output efficiency while high compliance. To verify the structural validity of the Funabot- Plate, two experiments were conducted to quantitatively compare its compliance characteristics with the rigid-frame robot. Through the measurement experiments, we were able to quantitatively evaluate that the structure of Funabot- Plate exhibited higher compliance in both conditions, transparent mode and assist mode, when compared to conventional rigid-frame robots.

关键词： Artificial muscles Actuators Tracking Force Wearable robots Pneumatic systems Soft robotics Assistive robots Wearable devices Robots

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Origami-Inspired Bistable Gripper with Self-Sensing Capabilities

Origami-Inspired Bistable Gripper with Self-Sensing Capabili...

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IEEE international conference on Soft robotics (RoboSoft)

作者： Christopher Kim Lele Yang Ashwath Anbuchelvan Raghav Garg Niv Milbar Flavia Vitale Cynthia Sung General Robotics Automation Sensing & Perception (GRASP) Lab at the University of Pennsylvania Philadelphia PA USA Department of Neurology Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Division of Plastic Surgery Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Department of Neurology Bioengineering and Physical medicine and Rehabilitation at the University of Pennsylvania Philadelphia PA USA

An origami-inspired bistable gripper, featuring a dual-function custom PET linear solenoid actuator that acts both as an actuator and a sensor, is presented. Movements in the permanent magnet plunger, which is directly mounted to the gripper, create induced electromotive force (emf) in the solenoid, and these induced emf measurements are used to detect snap-through actions and light contacts on the gripper. The fabrication methods for the gripper, actuator, and a gel-free soft wearable EMG electrode are outlined, and the actuator's self-sensing method utilizing the time-integral of the induced emf measurements are explored. Because a self-sensing actuator eliminates the need for extra sensors, it allows for further miniaturization of the robot while maintaining its compactness and lightweight design. The paper also introduces a full human-in-the-loop system, allowing users to open or close the gripper with their biceps via a wearable EMG electrode. This system bridges human intent with robotic action, offering a more intuitive interaction model for robotic control.

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Transformer-Based Soft Robot Constrained Position Recognition with Multimodal Sensing

Transformer-Based Soft Robot Constrained Position Recognitio...

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Intelligent robotics and Automatic Control (IRAC), international conference on

作者： Shouyan Chen Mingju Wang Yuxuan Guan Jiahao Cheng Diwei Zhao Zitian Wu Zhijie Liu Zhijia Zhao Mechanical and Electrical Engineering Guangzhou University Guangzhou China Department of Robot Engineering Guangzhou University Guangzhou China Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This study initially investigates the potential issues associated with single-modality sensing in soft robotic systems and the variations in multimodal sensing weights for robot state recognition under different conditions. Subsequently, we construct a transformer-based model for the recognition of constrained states in robots and compare its performance with several alternative methods, including LSTM, GRU, BiLSTM, and *** results show that the Transformer model outperforms methods such as LSTM, GRU, BiLSTM, and CNN-Att-LSTM in terms of accuracy and reliability, with an accuracy rate of over 90% in distinguishing between constrained and unconstrained states of soft robots.

关键词： Accuracy Runtime Multimodal sensors Neural networks Bidirectional long short term memory Soft robotics Predictive models Transformers Robot sensing systems Time factors

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