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IEEE International Conference on Industrial Informatics (INDIN)

作者： Izhar Oswaldo Escudero-Ornelas Divya Tiwari Michael Farnsworth Ze Zhang Ashutosh Tiwari Department of Automatic Control and Systems Engineering (ACSE) Sheffield UK

This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine Learning (SML) algorithm. The conventional End of the Line (EoL) tests are insufficient in detecting faults during process resulting in increased manufacturing costs and lead times. The proposed methodology utilises a Knowledge Distillation (KD) approach to address the challenges associated with the technique and optimise the student model's performance by employing architecture search and data augmentation. Multiple SML algorithms were evaluated to determine their effectiveness in predicting faults during manufacturing. The random forest algorithm demonstrated superior performance due to its ability to handle complex data and identify the impact of interdependencies of process parameters on the final product quality. The method was validated by conducting physical experiments on a linear coil-winding machine, and the results indicated that the random forest algorithm has the potential to decrease simulation time from 2 minutes to less than a second. The proposed methodology has the potential to reduce manufacturing time, enhance stator quality, and ultimately improve their reliability and safety.

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Radial basis function network-based optimization of the hard self-propelled rotary turning titanium

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Neural Computing and Applications 2025年第7期37卷 5607-5634页

作者： Nguyen, Trung-Thanh Dang, Xuan-Ba Faculty of Mechanical Engineering Le Quy Don Technical University 236 Hoang Quoc Viet Hanoi100000 Viet Nam Department of Automatic Control Ho Chi Minh City University of Technology and Education 1 Vo Van Ngan Ho Chi Minh City70000 Viet Nam

Machining of difficult-to-cut materials such as high-temperature metals is challenging due to their low machinability resulting in reduced productivity and high manufacturing cost. This investigation develops and optimizes the hard self-propelled rotary turning (HSPRT) operation, in which an efficient self-propelled rotary tool is proposed and fabricated. The HSPRT responses (total carbon emission—TC, machined roughness—MR, and noise emission—EN) are minimized using optimal process variables (inclination angle—A, turning depth—D, turning speed—V, and rake angle—R). The TC, MR, and EN models are developed in terms of the HSPRT inputs using the radial basis function network and response surface method, while the weights were computed using the removal effects of criteria, EQUAL, and rank order centroid methods. The improved quantum-behaved particle swarm optimization algorithm and method based on the multi-attributive border approximation area comparison were applied to produce feasible solutions and select the best optimal point. The optimization findings of the V, D, f, and R were 32 deg., 0.2 mm, 137 m/min, and 20 deg., while the TC, MR, and EN were saved by 42.8%, 24.1%, and 20.0%, respectively. The HSPRT performances were primarily affected by the turning depth and speed, respectively. The valuable outcomes could be applied to the practice to boost HSPRT performances, while the developed HSPRT operation could be utilized for machining alloys and hardened steels. The technique could be applied to treat optimization problems for other rotary turning processes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Particle swarm optimization (PSO)

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An Adaptive Pose controller of 6DOF Quadcopters Using a State Observer 6

An Adaptive Pose Controller of 6DOF Quadcopters Using a Stat...

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6th International Conference on Green technology and Sustainable Development, GTSD 2022

作者： Khanh, Nguyen Duy Van Quan, Phan Trong Phu, Ha Pham Ba, Dang Xuan HCMC University of Technology and Education Department of Automatic Control Ho Chi Minh City Viet Nam

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

Unmanned aerial vehicles (UAV, drones) are becoming one of the key machines/tools of the modern world, especially in military applications. Numerous research work is underway to explore the possibility of using these machines in other applications such as delivery, protecting wildlife, agricultural activities, academy, searching, and rescue missions. Since these machines are unmanned vehicles, their functionalities are completely dependent upon the performances of their control systems. This article presents a control approach to obtain better stabilization in the attitude and altitude of a quadcopter under different disturbance conditions. The controller proposed is contingent on a Linear Quadratic Regulator (LQR) structure with a state observer (SO). The combination of LQR controller and the state observer is used to control and filter out the sensors and systems noises. Therefore, the quadcopter can be controlled to result in expected control performances in numerous different environments. Finally, the effectiveness of the proposed method is demonstrated through simulation results. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Comparative study of deep learning models for action recognition based on skeleton data

Comparative study of deep learning models for action recogni...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Liang, Zhanhao Kudayberdievna, Kadyrkulova Kyial Isakunovich, Batyrkanov Jenish Liang, Zhantu Department of Automatic Control Kyrgyz State Technical University after Named I.Razzakov Bishkek Kyrgyzstan Faculty of Engineering Science and Technology Infrastructure University Kuala Lumpur Kajang Malaysia

ISBN: (纸本)9781510677685

In this study, we conducted a comparative analysis of three deep learning models - CNNs (90% accuracy), LSTMs (92% accuracy), and RNNs (95% accuracy) - for skeleton-based action recognition. The research focused on evaluating each model's ability to accurately classify a variety of human actions using a Kaggle dataset. Our findings revealed CNNs' strength in spatial recognition, LSTMs' proficiency in temporal dynamics, and RNNs' overall superiority in sequential processing. The study emphasizes the importance of model selection in action recognition tasks and lays groundwork for future exploration into hybrid models. © 2024 SPIE.

关键词： Classification (of information)

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Nonlinear Study of SRG Configurations Using Finite Element Analysis 23

Nonlinear Study of SRG Configurations Using Finite Element A...

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23rd International Middle East Power systems Conference, MEPCON 2022

作者： Abdo, Asmaa E. Ghoneim, Walid A. M. Ashour, Hamdy A. Arab Academy for Science Electrical Engineering and Automatic Control Department Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9781665463638

This paper, presents the non-linear analysis of the most common configurations of Switched Reluctance Generators (SRG). Finite Element Analysis (FEA) of two-phase (4/2), three-phase (6/4), and four-phase (8/6) SRGs have been carried out using (ANSYS Maxwell 16 - 3D Modeler - Transient solution). The simulation process was performed utilizing the Asymmetric Half Bridge Converter (AHBC) model as an external excitation circuit for the SRG model. Research interests have been dedicated recently to SRG as a promising and reliable candidate in both low-speed and high-speed applications, especially for small-scale renewable applications. However, this study includes the non-linear validation for both the conventional short-pitched and the fully-pitched SRG winding configurations at low-speed simulation conditions. This study aims to analyze the non-linear performance of SRG and validate the influence of stator and rotor pole angles (\beta{s} and \beta{r}) variation in addition to the excitation current patterns on the induced output voltage. The dynamic performance of SRG has been simulated, with the exact description of rotor angular position, number of conductors per phase, and the excitation current pattern for each case study. The FE simulation was applied to the same SRG test model;i.e., the same dimensions, the same angular speed, and the same number of conductors per phase. Moreover, SRG FEA output results have been recorded and compared to the corresponding results delivered from a related SRG linear analysis study done by the authors;hence the final proposed design was highlighted for further prototype implementation. The outcome of this study provides the recommended SRG design that can generate the maximum induced output voltage for the same machine design parameters. © 2022 IEEE.

关键词： Finite element method

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Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate control Chart

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Journal of Dynamics, Monitoring and Diagnostics 2023年第4期2卷 243-251页

作者： Yufei Gui Ziqiang Lang Zepeng Liu Hatim Laalej Department of Automatic Control and Systems Engineering University of SheffieldSheffield S102TNUK Advanced Manufacturing Research Centre University of SheffieldSheffield S102TNUK

Tool condition monitoring(TCM)is a key technology for intelligent *** objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing ***,an innovative TCM approach based on sensor data modelling and model frequency analysis has been *** from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process ***,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health *** order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency *** feature dimension is reduced by principal component analysis *** the moving average strategy is exploited to generate monitoring variables and overcome the effects of *** milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end *** results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

关键词： intelligent manufacturing multivariate control chart Nonlinear Autoregressive with eXogenous Input modelling Nonlinear Output Frequency Response Functions tool condition monitoring

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Operational Design Domain Qualification Framework for Remotely Driven Vehicles in Urban Environment

Operational Design Domain Qualification Framework for Remote...

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2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023

作者： Hans, Ole Avezum, Mariana Borysov, Stanislav Ross, Hans-Leo Adamy, Jurgen Vay Technology GmbH Berlin Germany Technical University of Darmstadt Department of Control Methods and Intelligent Systems Darmstadt Germany

ISBN: (纸本)9798350322538

This paper presents an Operational Design Domain (ODD) qualification framework for remotely driven vehicles taking into account safety and connectivity considerations. The framework consists of three stages: ODD investigation, ODD validation, and qualified ODD, with several quality gates between each stage. The safety considerations are established on a scenario-based testing approach, which helps identify potential hazards and assess risks concerning the ODD. Connectivity considerations are considered to ensure that the Remote Driving System (RDS) can operate within the given ODD while ensuring that the collected data is consistent. This is achieved by considering connectivity factors such as observed and predicted performance, which must meet pre-defined targets. The proposed ODD qualification framework is intended to provide a comprehensive and systematic approach for qualifying the ODD of RDS in an urban environment. This methodological framework is expected to support the safe and efficient operation of RDS in various applications and enable the removal of a human in-vehicle fallback. © 2023 IEEE.

关键词： Risk perception

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A Monitoring Strategy of Multi-Robot systems in Dynamic Environments: Coverage control and Density Estimation 11

A Monitoring Strategy of Multi-Robot Systems in Dynamic Envi...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Kaviri, Samane Tahsiri, Ahmadreza Taghirad, Hamid D. K.N. Toosi University of Technology Faculty of Electrical Engineering Department Od Systems and Control Tehran Iran

ISBN: (纸本)9798350308105

A critical task in environmental monitoring is the reconstruction of the density function of an event over the mission region. In such tasks, Unmanned Aerial Vehicles (UAVs) are employed as spatially distributed mobile sensing agents. In this paper, we consider the cooperative coverage problem for a group of UAVs in the mission region with a time-varying density function. The density function is modeled by a Gaussian Mixture Model (GMM) with unknown parameters. By resorting to a consensus-based Expectation-Maximization (EM) approach, the GMM parameters are estimated based on the local observations of UAVs. Finally, the proposed strategy is applied to an offshore oil spill monitoring mission. The simulation results verify the theoretical results and demonstrate the competence of the proposed strategy for such applications. © 2023 IEEE.

关键词： Multipurpose robots

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A Retractable Soft Growing Robot with a Flexible Backbone

A Retractable Soft Growing Robot with a Flexible Backbone

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

作者： Xinyi Pi Isabella A. Szczech Lin Cao Department of Automatic Control and Systems Engineering University of Sheffield UK

Soft-growing robots are emerging with numerous potential applications because of their superior capability of frictionless navigation. However, their success is hindered by their tendency to buckle under the tension required to retract them via inversion. In this paper, we propose a simple and scalable tubular backbone to facilitate retracting the robot body without buckling. With this backbone, compressive forces at the robot's tip are mitigated and a limit is placed on the effective length for retraction during the application of tension. We first present the selection of the backbone and the development of such a retractable soft-growing robot. Along with the characterization of the working principles behind this buckling-free mechanism, success was observed with the use of the backbone in retraction tests. The effects of different parameters such as robot body lengths, air pressures, curvatures, and retraction modes on the performance were also investigated. This backbone approach requires no bulky or in-situ mechatronic components inside the robot body and thus may be used in medical applications which appreciate simple, compact, and in-situ electronic-free designs.

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A Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators

A Machine-Learning Approach for Prognosis of Oscillating Wat...

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Circuits, systems, Communications and Computers (CSCC), International Conference on

作者： Izaskun Garrido Jon Lecube Fares Mzoughi Pavam Aboutalebi Irfan Ahmad Salvador Cayuela Ailtor Garrido Department of Automatic Control and Systems Engineering Automatic Control Group ACG University of the Basque Country-UPV/EHU Bilbao Spain

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbors (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. These results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive strategies in reducing operational costs and maximizing energy production.

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