检索结果-内蒙古大学图书馆

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： An, Haibo Liang, Wei Ding, Xiaojian Zhang, Yinlong Tan, Jindong State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Fifth Electronic Institute of MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

This paper presents a performance degradation analysis for rotate vector (RV) reducer using acoustic emission (AE) techniques. Unlike the AE signals from fixed gear reducer that are steady and have unchanged route, the AE signals from RV reducer have variant propagation paths and strengths. This work investigates the kinematics model using AE techniques to solve the inherent issues of RV reducer performance degradation analysis, such as signal variabilities and inappropriate thresholds. Firstly, the propagation path of AE signals within the crankshaft is developed. Secondly, the attenuation trend of AE signals is exponentially formulated based on the corresponding propagation curve. The proposed method has been evaluated on the developed RV reducer platform. AE signals were collected under various working conditions, i.e., four abrasion states with eight types of loads and five rotation speeds. The experimental analysis on typical parameters (e.g., signal energies) proves the effectiveness and accuracy of the developed method based on AE techniques. © 2019 IEEE.

关键词： Crankshafts

来源：评论

学校读者我要写书评

暂无评论

Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest 4th

Distributed Hierarchical Fault Diagnosis Based on Sparse Aut...

引用

4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Li, Tong Song, Chunhe Liu, Yang Wang, Zhongfeng Yu, Shimao Su, Shanting Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030323875

For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

An approach to extract state information from multivariate time series

引用

Journal of Computers (Taiwan) 2020年第6期31卷 1-11页

作者： Gui, Jun Zheng, Zeyu Qin, Zhaobo Jia, Danping Gao, Yuan Liu, Zhi Yao, Qingfeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang110016 China School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China

System behaviors could be recorded as multivariate time series by smart sensors. It is challenging to interpret such high-dimensional dataset with a one-dimensional temporal state sequence. In this paper, we propose a new approach DBSE (Distribution-based States Extraction) which is based on statistical and clustering analysis. State extraction problem could be resolved through using distribution parameters to describe each subsequence in multivariate time series and applying clustering method to extract states based on distribution similarity in order to obtain a temporal state sequence and information of each state. We validate DBSE and demonstrate how to use DBSE in real-world by extracting state information from a wearable sensor dataset (PAMAP2_Dataset). By comparing DBSE with TICC (Toeplitz Inverse Covariance-based Clustering) and FCM (Fuzzy C-means Clustering), the new approach is more accurate and effective. Moreover, DBSE is also expected to facilitate future behavior analysis. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

A Double-Sided Flat Permanent Magnet Linear Motor with Dual Halbach for Free-Piston Linear Generator

A Double-Sided Flat Permanent Magnet Linear Motor with Dual ...

引用

International Future Energy Electronics Conference (IFEEC)

作者： Feixue Chen Chi Zhang Jinhua Chen Jie Zhang Mengfei Wei ChenXia Xu Guilin Yang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology of Zhejiang Province Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Phase Motion Control Ningbo Ltd.

ISBN: (数字)9781728131535

ISBN: (纸本)9781728131542

Free-piston linear generator (FPLG) is regarded as a promising power generator system for hybrid electric vehicle. The core component of FPLG, linear motor, requires careful design to match the system performances. In this paper, for required performance, a novel double-sided flat permanent magnet linear motor with dual Halbach is proposed. This motor is composed of four modular unit stators on both sides of the mover. Fractional-slot (7-poles/6-slot) and concentrated windings are adopted to each unit for high power, low thrust ripple. The relationship between the linear motor performance and structure parameters is developed and applied to design the size of linear motor. To verify the design of this motor, the two-dimensional finite-element model is established. The feature of output power in motoring and generating model are analyzed. In addition, a prototype is developed and tested in an experimental platform, which consists of rotating electrical machine and crankshaft connecting rod system. In terms of system output power, the experimental data is in well agreement with the finite-element results.

关键词： Permanent magnet motors Permanent magnets Windings Power generation Hybrid electric vehicles Stators Force

来源：评论

学校读者我要写书评

暂无评论

Interleaved Boost PFC control strategy based on repetitive control

Interleaved Boost PFC control strategy based on repetitive c...

引用

International Conference on Electrical Machines and Systems (ICEMS)

作者： JiaQi Zheng Tao Zhang Hualiang Zhang Pengpeng Ren Rongkai Su College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Network Control System Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781728133997

In this paper, the working principle of the staggered parallel Boost power factor correction (PFC) circuit is studied. The main circuit adopting the topology of the rectifier device has the following problems: the input current contains higher harmonics, the control system has poor steady-state performance, and the dynamic performance is low. The current loop of the control system adopts the improved repetitive PI double loop control instead of the traditional PI control method to realize the no-difference tracking of the higher harmonics, and to effectively suppress the higher harmonics contained in the output current, so that the control system has good steady-state performance; Combining repetitive control with PI control to effectively compensate for the dynamic performance reduced by the repetitive control. In order to verify the effectiveness of the proposed method, the improved repeated PI double loop control method is compared with the traditional PI control method. The simulation results show that the improved repeated PI double loop control can improve the input current waveform of the rectifier, improve the power factor of the converter, and reduce the THD value of the input current of the rectifier device.

关键词： Voltage control control systems Rectifiers Steady-state Inductors Reactive power Frequency control

来源：评论

学校读者我要写书评

暂无评论

Exponential Observers for Discrete-Time Nonlinear Systems with Incremental Quadratic Constraints

Exponential Observers for Discrete-Time Nonlinear Systems wi...

引用

American control Conference

作者： Wei Zhang Younan Zhao Masoud Abbaszadeh Mingming Ji Xiushan Cai Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science Shanghai 201620 China GE Global Research Niskayuna New York 12309 USA Zhejiang Normal University Jinhua 321004 China

ISBN: (纸本)9781538679012;9781538679265

This paper investigates the observer design for a class of nonlinear discrete-time systems satisfying incremental quadratic constraints. A circle criterion based full-order observer is constructed by injecting output estimation error into the observer nonlinear terms. We also construct a reduced-order observer to estimate the unmeasured system state. The proposed observers guarantee exponential convergence of the estimation error to zero. The design of the proposed observers is reduced to solving a set of linear matrix inequalities. It is proved that the conditions under which a full-order observer exists also guarantee the existence of a reduced-order observer. Compared to some previous results in the literature, this work considers a larger class of nonlinearities and unifies some related observer designs for discrete-time nonlinear systems. Finally, a numerical example is given to illustrate the effectiveness of the proposed design.

关键词： Observers discrete-time nonlinearity Circle criterion discrete time systems Incremental Quadratic Exponential Estimation error linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular robotics

arXiv

引用

arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Industrial robot rotate vector reducer fault detection based on hidden markov models

Industrial robot rotate vector reducer fault detection based...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yinlong An, Haibo Ding, Xiaojian Liang, Wei Yuan, Mingze Ji, Chunyang Tan, Jindong Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Fifth Electronic Instituteof MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the long-term maintenance of high-precision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal pre-processings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults. © 2019 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

An Abnormal State Forecasting Method for Substation Equipment based on Improved LSTM

An Abnormal State Forecasting Method for Substation Equipmen...

引用

2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Li, Tong Yu, Shimao Song, Chunhe Yu, Tongwei Zeng, Peng Shen, Li State Grid Liaoning Electric Power Co. Ltd. Liaoning Electric Power Research Institute Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

ISBN: (纸本)9781728149301

Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep learning have been put forward in recent years, but they have deficiencies dealing with temporal features. This paper proposes an abnormal forecasting method for substation equipment based on improved LSTM, and temporal features of power equipment fault can be extracted effectively for a long-time span. The method reads secondary equipment information through SCADA system, then extracts feature to carry out deep learning training. In this paper, RNN and LSTM are firstly compared, and then the structure and algorithm of improved LSTM are given. This method increases coupling between forget gate and cell state compared with the standard LSTM structure, so as to make better use of information at previous moment. Meanwhile problems of network training and overfitting are discussed. Experiments are conducted to demonstrate that improved LSTM is capable of forecasting accurately the complex electric equipment state with a long forecasting horizon. The accuracy of abnormal state forecasting can reach more than 93% which has a better performance compared with many other methods. © 2019 IEEE.

关键词： SCADA systems

来源：评论

学校读者我要写书评

暂无评论

Simultaneous state and parameter estimation: The role of sensitivity analysis

arXiv

引用

arXiv 2020年

作者： Liu, Jianbang Gnanasekar, Aristarchus Zhang, Yi Bo, Song Liu, Jinfeng Hu, Jingtao Zou, Tao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute for Robotics & Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Key Laboratory of Energy Thermal Conversion & Control Southeast University Nanjing210096 China School of Mechanical and Electrical Engineering Guangzhou University Guangzhou510006 China

State and parameter estimation is essential for process monitoring and control. Observability plays an important role in both state and parameter estimation. In simultaneous state and parameter estimation, the parameters are often augmented as extra states of the original system. When the augmented system is observable, various existing state estimation approaches may be used to estimate the states and parameters simultaneously. However, when the augmented system is not observable, how we should proceed to maximally extract the information contained in the measured outputs is not clear. This paper concerns about simultaneous state and parameter estimation when the augmented system is not fully observable. Specifically, we first show how sensitivity analysis is related to observability of a dynamical system, and then illustrate how it may be used to select variables for simultaneous estimation. We also propose a moving horizon state estimation (MHE) design that can use the variable selection results in a natural way. Extensive simulations are carried out to show the efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：