2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)最新文献

Experimental Study on Rubbing Failure of Tilting Pad Bearing in Heavy Duty Gas Turbine 重型燃气轮机倾斜垫轴承摩擦失效试验研究

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9941760

Yongzhi Feng, Yushu Chen, Ning Yu, Fangang Meng, Qian Jia, Xiaoyang Yuan

引用次数: 0

Automatic Detection System of Substation Relay Protection Device Based on Failure Density Function 基于故障密度函数的变电站继电保护装置自动检测系统

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942210

Shuang Chen, Xi Yang, Zhen Gao, Xun Cui, Rong Liang, Lan Cheng

{"title":"Automatic Detection System of Substation Relay Protection Device Based on Failure Density Function","authors":"Shuang Chen, Xi Yang, Zhen Gao, Xun Cui, Rong Liang, Lan Cheng","doi":"10.1109/PHM-Yantai55411.2022.9942210","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942210","url":null,"abstract":"Because the traditional substation relay protection device automatic detection system has the problem of inaccurate detection results, the substation relay protection device automatic detection system based on the failure density function is designed. The hardware structure of the system is designed by the high-performance C8051F040 single chip microcomputer and the insulation detection sensor. On the basis of the system hardware design, the system software function is optimized. According to the relay protection current setting calculation flow, the genetic algorithm is derived to construct the failure density function, and the automatic detection of the relay protection device in the substation is carried out, Through the hardware design and software design of the system, the design of automatic detection system of substation relay protection device based on genetic algorithm is completed. The experimental results show that the designed system can detect the insulation voltage of positive and negative bus more accurately and has high practicability in the practical application process, which fully meets the research requirements.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276252","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

Aeroengine Robust Component Model Design Based on 3D Virtual Design 基于三维虚拟设计的航空发动机鲁棒部件模型设计

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942002

Dan Zhao, Ming-fei Qu

引用次数: 0

A Microfluidic Oil Particles Monitoring System based on Raspberry Pi 基于树莓派的微流控油颗粒监测系统

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9941791

Zhenzhen Liu, Yan Liu, Hongfu Zuo, Han Wang, Hang Fei, Zhiqiang Jiang

引用次数: 0

A Novel Maximum-Entropy Bayesian Integration Approach for Reliability Analysis 可靠性分析中一种新的最大熵贝叶斯积分方法

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942055

Bowen Li, Bingyi Li, Jiahui He, Hongbin Liu, X. Jia, B. Guo

{"title":"A Novel Maximum-Entropy Bayesian Integration Approach for Reliability Analysis","authors":"Bowen Li, Bingyi Li, Jiahui He, Hongbin Liu, X. Jia, B. Guo","doi":"10.1109/PHM-Yantai55411.2022.9942055","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942055","url":null,"abstract":"Reliability analysis based on data from various source is common today. Bayes theory is proved effectively in integrating prior information and field information. However, the complicated calculation and limited applicability have a negative effect on solution. And the fusion is imbalanced in some case. This paper investigates a novel approach to integrate degradation data and lifetime data for reliability analysis. Firstly, inverse Gaussian process model is adopted to model the degradation and the crude estimation can be solved by degradation data. After that, a constrained maximum-entropy Bayesian integration model is proposed for exploring more information from reliability life test. For simplifying the calculation, a pivot variable, failure probability, is defined and updated in this model. This allows us to derive the model parameters by fitting the failure probability curve rather than the calculation on Bayes posterior distribution. Accordingly, the reliability assessment can be conducted based on the inverse Gaussian process model. A case study illustrates the validity and improvement of the proposed method.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116551044","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

Simulation of seasonal variation characteristics of offshore water temperature based on ROMS model 基于ROMS模型的近海水温季节变化特征模拟

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942034

S. Han, Zhong-Min Wang

引用次数: 2

A Component Importance Measure Considering System Topology 一种考虑系统拓扑的构件重要性度量方法

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942208

Min Luo, Yimiao Yao

引用次数: 0

Comprehensive Evaluation Method of Moral Education in Colleges And Universities Based on Viterbi Algorithm 基于Viterbi算法的高校德育综合评价方法

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/PHM-Yantai55411.2022.9942214

Xinjiu Liang, Shuilan Song

{"title":"Comprehensive Evaluation Method of Moral Education in Colleges And Universities Based on Viterbi Algorithm","authors":"Xinjiu Liang, Shuilan Song","doi":"10.1109/PHM-Yantai55411.2022.9942214","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942214","url":null,"abstract":"In view of the relatively poor index selection and sorting ability of the current comprehensive evaluation method of moral education in colleges and universities, which leads to serious distortion of the evaluation results, a comprehensive evaluation method of moral education in colleges and universities is proposed. Using the Viterbi method to analyze the factors that affect the quality of moral education of college students, an index system for the evaluation of moral education in colleges is established. Using the analytic hierarchy process, on this basis, the weight of the comprehensive evaluation index of university moral education is calculated. The fuzzy comprehensive evaluation method is used to construct a comprehensive evaluation model of moral education in colleges and universities, and obtain the evaluation results. So far, the design of the comprehensive evaluation method of moral education in colleges and universities based on the Viterbi algorithm is completed. The experimental link is constructed, and the experimental results confirm that the evaluation results of this method have high reliability and are better than the evaluation effect of the current method.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129785217","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

Fault Diagnosis of Rotating Machinery Based on FMEA and Zero-shot Learning 基于FMEA和零弹学习的旋转机械故障诊断

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/phm-yantai55411.2022.9942104

Boyang Zhao, Tong Li, Wei Dai, Junjun Dong

{"title":"Fault Diagnosis of Rotating Machinery Based on FMEA and Zero-shot Learning","authors":"Boyang Zhao, Tong Li, Wei Dai, Junjun Dong","doi":"10.1109/phm-yantai55411.2022.9942104","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9942104","url":null,"abstract":"Data-driven intelligent fault diagnosis is now a research hotspot. However, the fault data collected in actual working conditions is limited, which leads to a decline in the diagnostic ability of fault diagnosis models that rely on balanced data in actual engineering. Fortunately, the zero-shot problem for some fault classes can be solved by transferring zero-shot learning from machine vision to rotating machinery fault diagnosis. Inspired by this, we propose an attribute description method based on Failure Mode and Effects Analysis (FMEA) to solve the problem of semantic description of rotating machinery fault data, so as to be used for zero-shot fault diagnosis of rotating machinery. The framework analyzes the failure modes of rotating machinery based on FMEA, and establishes a fault attribute dictionary. After that, the attribute classifier is trained using the multi-domain features of the data. Finally, the fault data of unknown class is diagnosed based on Euclidean distance. The effectiveness of the framework is verified by public datasets. This framework provides a new perspective for zero-shot fault diagnosis of rotating machinery.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128373819","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

Deep Residual Net-Based Prognosis Method for Lithium-ion Batteries with Information Fusion from Different Scales 基于深度残差网络的不同尺度信息融合锂离子电池预测方法

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai) Pub Date : 2022-10-13 DOI: 10.1109/phm-yantai55411.2022.9941914

Yafei Zhu, Xiang Li, Wei Zhang

{"title":"Deep Residual Net-Based Prognosis Method for Lithium-ion Batteries with Information Fusion from Different Scales","authors":"Yafei Zhu, Xiang Li, Wei Zhang","doi":"10.1109/phm-yantai55411.2022.9941914","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941914","url":null,"abstract":"Nowadays, prognosis methods based on deep learning have been successfully developed and applied in many industrial fields, such as energy, transportation, aero-space engineering etc. Lithium-ion battery prognosis is very important to indicate the health states of the energy system, which has been a hot topic in the past decades. In this paper, a new method is proposed for battery prognosis. The proposed architecture integrates the traditional DNN and CNN models, and divides the feature graph into two branches for separate computation. Residual network is also used in the prediction model for pursuing better effects. Residual block is implemented by a hidden layer connecting each branch’s inputs and outputs, which improves the model’s generalization ability. The proposed method takes up one-step prediction for CALCE lithium-ion data set. Experimental results show that the proposed method has a better prediction effect. Therefore, it is of great significance to predict the life of lithiumion batteries and become a new basis for a deep learning-based method to predict the life of lithium-ion batteries.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124720881","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