2022 Prognostics and Health Management Conference (PHM-2022 London)最新文献_第9页

Edge Node Information Model under the Framework of Edge Computing 边缘计算框架下的边缘节点信息模型

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00078

Xing Gao, Zhirong Tan, Gang Xing

引用次数: 0

A Multi-Target Tracking and Positioning Technology for UAV Based on Siamrpn Algorithm 基于Siamrpn算法的无人机多目标跟踪定位技术

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00086

Ligang Wu, Changxing Zhao, Zushan Ding, Xiao Zhang, Yiding Wang, Yang Li

引用次数: 0

Research on DC Microgrid Simulation for Marine Energy and Implementation of RT-LAB Semi-physical Simulation 海洋能源直流微电网仿真研究及RT-LAB半物理仿真实现

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00022

Zhiyong Zhou, Liqiang Zhang, Xianye Zhu, Zhen Chen, Ming Li

引用次数: 0

Defending Against Adversarial Attacks on Time- series with Selective Classification 基于选择性分类的时间序列对抗性攻击防御

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/phm2022-london52454.2022.00038

J. Kuhne, C. Guhmann

引用次数: 1

SOC Estimation for Lithium-ion Battery Based on Model-in-the-Loop for Embedded System Test 基于在环模型的嵌入式系统测试锂离子电池荷电状态估计

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00023

Yu Xu, Liqiang Zhang, Xianye Zhu, Xiangyu Wang, Ming Li

引用次数: 0

Recent Research and Applications in Variational Autoencoders for Industrial Prognosis and Health Management: A Survey 变分自编码器在工业预测和健康管理中的研究与应用

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00042

R. Zemouri, M. Lévesque, Étienne Boucher, M. Kirouac, François Lafleur, Simon Bernier, A. Merkhouf

{"title":"Recent Research and Applications in Variational Autoencoders for Industrial Prognosis and Health Management: A Survey","authors":"R. Zemouri, M. Lévesque, Étienne Boucher, M. Kirouac, François Lafleur, Simon Bernier, A. Merkhouf","doi":"10.1109/PHM2022-London52454.2022.00042","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00042","url":null,"abstract":"Whether in the industrial, medical, or real-world domains, more and more data are being collected. The common particularity of all these application domains is that a great part of this data is mostly unlabeled. Thus, designing a learning model with a minimum of labeled data represents a major challenge in the coming years. A particular emphasis has recently been put on unsupervised learning methods based on the idea of autoencoding. The objective of these methods is twofold: to reduce the dimensionality of the input space and to reconstruct the original observation from this lower dimensional representation space. The variational form of these autoencoders, called the Variational Autoencoders (VAEs), is particularly successful in almost all application areas. This enthusiasm comes from the fact that VAEs allow to take advantage of the theoretical foundations of the Variational Bayesian methods and the learning capabilities of artificial neural networks. This review paper gives to the PHM community a synthesis of the latest publications in the PHM domain using the VAEs related to four topics: 1) Data-Driven Soft Sensors for missing values and data outliers, 2) reconstruction error for fault detection, 3) resampling approach for imbalanced data generation and minority class and 4) the variational embedding as PHM preprocessing pipelines and data transformations. After a review of the theoretical foundations and some practical tricks to succeed the implementation of the VAEs in industrial applications, the four main topics used to exploit the VAEs in the PHM domain are detailed. Finally, a global view of the research done at the research institute of Hydro-Québec regarding the diagnosis and failure detection of hydro-generators with VAEs are presented.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"374 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809473","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: 3

CNC Machining Quality Prediction Using Variational Autoencoder: A Novel Industrial 2 TB Dataset 使用变分自编码器的数控加工质量预测:一个新的工业2tb数据集

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00069

Antoine Proteau, R. Zemouri, Antoine Tahan, Marc Thomas, Wafa Bounouara, Stéphane Agnard

{"title":"CNC Machining Quality Prediction Using Variational Autoencoder: A Novel Industrial 2 TB Dataset","authors":"Antoine Proteau, R. Zemouri, Antoine Tahan, Marc Thomas, Wafa Bounouara, Stéphane Agnard","doi":"10.1109/PHM2022-London52454.2022.00069","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00069","url":null,"abstract":"The purpose of this paper is to present and to describe a novel dataset acquired entirely in an industrial environment during multiple regular scheduled production runs. In the monitoring, prognostic and fault detection literature, researchers are often faced with work based on the same popular datasets; for instance, the milling dataset, the Pronostia Bearing Dataset, the IMS Bearing Dataset or the Turbofan Engine Degradation Simulation Dataset. On the one hand, these datasets are the results of either simulations or acquired in a laboratory under controlled environment. On the other hand, a real industrial context might not be adequately represented within these datasets due to less controlled parameters or increased complexity. Consequently, it becomes critical to have access to a way to test and validate research work on both experimental and industrial data. In that mindset, to accelerate the technological transfer to the industry and to ensure that it can quickly profit from the benefits that the monitoring, diagnostic and prognostic research area can provide them, a new dataset acquired at an industrial partner: a machining company located in Quebec City (Qc, Canada) is presented.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133369464","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: 0

A Fault Diagnosis Platform of Actuators on Embedded IoT Microcontrollers 嵌入式物联网微控制器执行器故障诊断平台

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00044

Shaowei Chen, Yanping Huang, Pengfei Wen, Chunyue Gu, Shuai Zhao

{"title":"A Fault Diagnosis Platform of Actuators on Embedded IoT Microcontrollers","authors":"Shaowei Chen, Yanping Huang, Pengfei Wen, Chunyue Gu, Shuai Zhao","doi":"10.1109/PHM2022-London52454.2022.00044","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00044","url":null,"abstract":"In the process of monitoring and fault diagnosis of complex electromechanical equipment, the close coupling between the fault diagnosis process and the front-end equipment can effectively reduce the occurrence of serious faults and significantly improve the economic benefits. In this paper, an Internet of Things (IoT) framework for monitoring and diagnosing industrial equipment is designed and implemented for complex electromechanical equipment running in real-time. All the procedures are physically implemented on a hardware prototype, which includes hardware selection, software configuration, transplanting of machine learning (ML) model and data communication. The framework of the physical platform is universal and flexible. It can be deployed in various monitoring scenarios, and flexibly customize the deployed artificial intelligence (AI) models according to their applications. Three typical machine learning algorithms of SVM, ANN and LSTM models are transplanted to STM32 MCU to compare the results. Finally, the proposed method is experimentally validated on NASA Electro-mechanical actuators (EMAs) data set.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451577","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

Implementation strategy of predictive maintenance in nuclear power plant 核电厂预测性维修的实施策略

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00033

Rui Han, Ping Li, Ziyu Shi

{"title":"Implementation strategy of predictive maintenance in nuclear power plant","authors":"Rui Han, Ping Li, Ziyu Shi","doi":"10.1109/PHM2022-London52454.2022.00033","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00033","url":null,"abstract":"Predictive maintenance of nuclear power plants is the continuous (or regular) condition monitoring and fault diagnosis of important parts of nuclear safety equipment when they are in operation. By judging the state of these equipment, predicting future development trends, making predictive maintenance plans based on development trends and possible failure modes, and determining the time, content, and method of equipment maintenance, as well as the required technical and material support [4]. Implementing predictive maintenance will help to detect early functional degradation, identify weak links in the operation process, and take timely intervention measures to enhance the safety of nuclear power plants, reduce the outage of nuclear power plants, and reduce operation and maintenance costs. The predictive maintenance of nuclear power plants relies on the \"Predictive Maintenance Program\", which stipulates the content and technology of predictive maintenance. According to the operation and maintenance experience of nuclear power units, the implementation of the predictive maintenance program should at least include three aspects: operation status assessment, equipment status assessment and maintenance implementation strategy. This paper mainly introduces the content of the predictive maintenance program of nuclear power plants, the content and process of the program implementation, and provides technical reference for other nuclear power plants to establish predictive maintenance.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044641","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

Using Semantic Dependencies to Realize the Construction of Cloud Data Center Operation and Maintenance Knowledge Graph 利用语义依赖关系实现云数据中心运维知识图谱的构建

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00049

Fenggang Lai, Z. Zhao, Dequan Gao, Wang Luo, Chao Lou, Shengya Han, Chao Ma

引用次数: 0