Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)最新文献_第2页

Lifetime prediction for electrical connector under the action of random vibration loading 随机振动载荷作用下电连接器寿命预测

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228872

Zhuang Chongyang, F. Qiang, Sun Bo

{"title":"Lifetime prediction for electrical connector under the action of random vibration loading","authors":"Zhuang Chongyang, F. Qiang, Sun Bo","doi":"10.1109/PHM.2012.6228872","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228872","url":null,"abstract":"Electrical connectors play a critical role on systems' reliability and vibration loading is the critical environmental factor which affects the reliability and lifetime of them. The existing methods to study the lifetime of electrical connectors under the action of vibration loading cannot quantify the response of the electrical connectors, cost high and time-consuming. To solve these problems, the method combined finite element (FE) simulation with failure physics equation was proposed. It can not only be used to quantify the vibration response, predict the lifetime and quantify the limited vibration loading of electrical connectors, but also analysis the reliability and optimization of electrical connector. The response of electrical connectors under external vibration excitation was quantified by FE modeling and simulation first. The relationship between external input vibration loading and local vibration response was established by multiple sets of vibration simulation and data fitting. Then the equation between lifetime of electrical connector and external vibration excitation was established combined with the failure physics equation. Finally, the test data was used to validate the lifetime of electrical connector.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445306","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: 5

Improved reliability approximate method combining Kriging and importance sampling 结合Kriging和重要抽样的改进可靠性近似方法

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228862

Zhan Liu, Jianguo Zhan, Chunlin Tan

引用次数: 4

On reducing feature dimensionality for partial discharge diagnosis applications 局部放电诊断中特征降维的研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228839

Weizhong Yan

引用次数: 3

A crossing rate method to reliability analysis of antenna structure under fluctuating wind 脉动风作用下天线结构可靠性分析的交叉率法

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228856

Cancan Wang, Jianguo Zhan, Zhan Liu, Xianchao Wang, Feng Wang

引用次数: 2

A comparison study of hidden Markov model and particle filtering method: Application to fault diagnosis for gearbox 隐马尔可夫模型与粒子滤波方法在齿轮箱故障诊断中的比较研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228865

Yunxian Jia, LeiĀ Sun, H. Teng

{"title":"A comparison study of hidden Markov model and particle filtering method: Application to fault diagnosis for gearbox","authors":"Yunxian Jia, LeiĀ Sun, H. Teng","doi":"10.1109/PHM.2012.6228865","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228865","url":null,"abstract":"For gearbox fault diagnosis, it is expected that a desired fault diagnosis model should have good computation efficiency, and have good recognition ability in both fault detection domain and fault identification domain. Currently, there are mainly three type's models in this area that are physical based model, artificial intelligence based model and data-driven based model. However, the first type model requires specific mechanistic knowledge and theory relevant to the monitored system structure which are hardly to realize; and the second type model needs large amounts of condition monitoring data which are also not always available; while data-driven model investigate proper statistical model to describe system state which is used widely in fault diagnosis domain. The purpose of this paper is to investigate two popular algorithms of date-driven models for gearbox fault diagnosis, namely hidden Markov model and particle filtering method. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. Then we respectively proposed hidden markov model and particle filtering model for fault diagnosis. Finally, the comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that particle filtering method has better detection performance, while hidden markov model has better computation efficiency in this area.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123459184","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: 12

A case study on battery life prediction using particle filtering 基于粒子滤波的电池寿命预测实例研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228847

Yinjiao Xing, E. W. M. Ma, K. Tsui, M. Pecht

引用次数: 21

Research on the health status monitoring model and monitoring system of destruction equipment for high-risk goods based on the fuzzy combination mode 基于模糊组合模式的高风险物品销毁设备健康状态监测模型及监测系统研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228825

Hongyuan Zhang, Yanliang Li, Jing Zhang

{"title":"Research on the health status monitoring model and monitoring system of destruction equipment for high-risk goods based on the fuzzy combination mode","authors":"Hongyuan Zhang, Yanliang Li, Jing Zhang","doi":"10.1109/PHM.2012.6228825","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228825","url":null,"abstract":"The high-risk and complexity of highly dangerous goods destruction need urgently a deep analysis and judgment to the variety health status of destroyed equipment in operation to ensure the operations safety,so the health status monitoring model of destruction equipment was constructed. In this paper it determined the operating status monitoring parameters and equipment unit parameters by reliability and pre-risk analysis, studied the health trends judgment methods of operating status parameters by statistical process control (SPC) and equipment PLC combination, the health status analysis methods of the operating state parameters subsystem related equipment based on the Profust. Finally it composed both analysis methods to judge the health status of destroyed equipment. While it builds the health status monitoring network of destruction equipment, the hardware and software part of actual destruction system. The model provides a new way for the establishment of high-risk operations equipment health status monitoring systems based on PHM.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117175246","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

OBDD-based algorithm for reliability evaluation of Wireless Sensor Networks 基于obdd的无线传感器网络可靠性评估算法

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228972

Bo Zhao, Yan Liu, Yufeng Xiao

引用次数: 2

Study on the support resources configuration of multi-aircraft formation 多机编队保障资源配置研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228894

Lin Ma, Qian Wu, Lei Li, Chao Lian

引用次数: 2

Test point selection strategy under unreliable test based on heuristic particle swarm optimization algorithm 基于启发式粒子群优化算法的不可靠测试点选择策略

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228884

D. Sen, Jing Bo, Yang Zhou

引用次数: 3