2022 International Conference on Control, Automation and Diagnosis (ICCAD)最新文献_第2页

Economic Admissibility Study on Small Hydro 小水电经济可采性研究

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9854042

Rodrigo Calhandro, R. Melício, Victor M. F. Mendes, Pedro Areias

引用次数: 0

A Neural Network Approach for an Automatic Detection and Localization of an Open Phase Circuit of a Five-Phase Induction Machine Used in a Drivetrain of an Electric Vehicle 电动汽车传动系统中五相感应电机开相电路的神经网络自动检测与定位

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853930

S. Chahba, R. Sehab, A. Akrad, C. Morel

{"title":"A Neural Network Approach for an Automatic Detection and Localization of an Open Phase Circuit of a Five-Phase Induction Machine Used in a Drivetrain of an Electric Vehicle","authors":"S. Chahba, R. Sehab, A. Akrad, C. Morel","doi":"10.1109/ICCAD55197.2022.9853930","DOIUrl":"https://doi.org/10.1109/ICCAD55197.2022.9853930","url":null,"abstract":"Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.","PeriodicalId":436377,"journal":{"name":"2022 International Conference on Control, Automation and Diagnosis (ICCAD)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126903645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sliding Mode Control of DFIG for a Variable Speed Wind Turbine 变转速风力机DFIG滑模控制

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9854007

Bechir Fatnassi, B. Torchani, A. Sellami, G. García

引用次数: 0

Synthesis of Diagnostic Observers for Linear Systems Based on Optimal Control Methods 基于最优控制方法的线性系统诊断观测器的综合

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853673

A. Kabanov, A. V. Zuev, A. Zhirabok, V. Filaretov

引用次数: 1

Deep Learning-Based Fault Detection and Isolation in Solar Plants for Highly Dynamic Days 基于深度学习的太阳能电站高动态天气故障检测与隔离

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853987

Sara Ruiz-Moreno, A. Gallego, Adolfo J. Sánchez, E. Camacho

{"title":"Deep Learning-Based Fault Detection and Isolation in Solar Plants for Highly Dynamic Days","authors":"Sara Ruiz-Moreno, A. Gallego, Adolfo J. Sánchez, E. Camacho","doi":"10.1109/ICCAD55197.2022.9853987","DOIUrl":"https://doi.org/10.1109/ICCAD55197.2022.9853987","url":null,"abstract":"Solar plants are exposed to numerous agents that degrade and damage their components. Due to their large size and constant operation, it is not easy to access them constantly to analyze possible failures on-site. It is, therefore, necessary to use techniques that automatically detect faults. In addition, it is crucial to detect the fault and know its location to deal with it as quickly and effectively as possible. This work applies a fault detection and isolation method to parabolic trough collector plants. A characteristic of solar plants is that they are highly dependent on the sun and the existence of clouds throughout the day, so it is not easy to achieve methods that work well when disturbances are too variable and difficult to predict. This work proposes dynamic artificial neural networks (ANNs) that take into account past information and are not so sensitive to the variations of the plant at each moment. With this, three types of failures are distinguished: failures in the optical efficiency of the mirrors, flow rate, and thermal losses in the pipes. Different ANNs have been proposed and compared with a simple feedforward ANN, obtaining an accuracy of 73.35%.","PeriodicalId":436377,"journal":{"name":"2022 International Conference on Control, Automation and Diagnosis (ICCAD)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131200103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adaptive Large Neighborhood Search for the Just-In-Time Job-shop Scheduling Problem 即时作业车间调度问题的自适应大邻域搜索

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853973

Abderrazzak Sabri, Hamid Allaoui, Omar Souissi

引用次数: 0

Blockchain Information Based Systems in Aviation: The Advantages for Aircraft Records Management 基于区块链信息的航空系统:飞机记录管理的优势

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853888

Luís F. F. M. Santos, José António Costa, Duarte Valério, N. Batista, R. Melício

引用次数: 0

Unsupervised Gear Monitoring using Deep Convolutional Auto-Encoders and K-Means : Application to Gotix Dataset 使用深度卷积自编码器和K-Means的无监督齿轮监测:在gotx数据集上的应用

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853913

Hind Kanj, A. Raad, D. Abboud, Y. Marnissi

引用次数: 0

1D ResNet for Fault Detection and Classification on Sensor Data in Semiconductor Manufacturing 半导体制造传感器数据故障检测与分类的一维ResNet方法

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9853997

Philip Tchatchoua, G. Graton, M. Ouladsine, Julien Muller, Abraham Traoré, Michel Juge

引用次数: 3

Optimized Model Predictive Control for Unmanned Aerial Vehicles with Sensor Uncertainties 具有传感器不确定性的无人机优化模型预测控制

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI: 10.1109/ICCAD55197.2022.9869421

A. Azar, Fernando E. Serrano, Nashwa Ahmad Kamal, B. Qureshi, Ammar K. Al Mhdawi, A. Humaidi, I. Ibraheem, Chakib Ben Njima

{"title":"Optimized Model Predictive Control for Unmanned Aerial Vehicles with Sensor Uncertainties","authors":"A. Azar, Fernando E. Serrano, Nashwa Ahmad Kamal, B. Qureshi, Ammar K. Al Mhdawi, A. Humaidi, I. Ibraheem, Chakib Ben Njima","doi":"10.1109/ICCAD55197.2022.9869421","DOIUrl":"https://doi.org/10.1109/ICCAD55197.2022.9869421","url":null,"abstract":"In this paper, a novel control strategy based on model predictive control is proposed for tracking the trajectory of unmanned aerial vehicles (UAV). First, the dynamic model of an unmanned aerial vehicle UAV is established by considering the linear velocities in terms of the analyzed system’s attitude angles. The UAV’s dynamic model is suitable for kinematic control by using two attitude angles as control inputs. The controller design is made up of an output feedback controller formed by the error variables, which are the difference between the measured output and the reference variables. Then, a model predictive control is established by considering an optimization problem with a performance index and the corresponding constraints. Furthermore, the stochastic properties of the UAV dynamic model are taken into account in this research study. The measured outputs provided by the sensors are constrained by the topological characteristics of the unmanned aerial vehicle to be analyzed. Finally, a numerical experiment and its conclusions are presented, demonstrating that the proposed control strategy provides optimal trajectory tracking.","PeriodicalId":436377,"journal":{"name":"2022 International Conference on Control, Automation and Diagnosis (ICCAD)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2