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

Research on storage life prediction method for strapdown inertial navigation system 捷联惯导系统存储寿命预测方法研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228878

Kai Luo, Liming Han, Hongzheng Fang, Shu-Lan Hou

{"title":"Research on storage life prediction method for strapdown inertial navigation system","authors":"Kai Luo, Liming Han, Hongzheng Fang, Shu-Lan Hou","doi":"10.1109/PHM.2012.6228878","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228878","url":null,"abstract":"For the high reliability and low cost benefits, the strapdown inertial navigation systems are widely applied to weapon systems. During the using course, the reliability and performance of strapdown inertial navigation system will degenerate periodically. As known, the system has service life which provided by fabricant, but it also has storage life which is influenced by the storage elements, such as temperature, humidity, vibration and so on. In this paper, we discuss the storage life prediction method for strapdown inertial navigation system by the reliability life test. Above all, we analyze the theory of storage life prediction for the strapdown inertial navigation system, mainly about the relationship of the navigation system's fault, nature element and storage life. Then the system storage life prediction method is researched based on statistics model and performance degenerate model. At last, these prediction algorithms are illustrated by the practical reliability life test data, and the applicability of every algorithm is discussed for strapdown inertial navigation system. These methods were applied in a practical equipment maintenance support project, which can provide technical support for the product reliability and life prolong research in the future.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"249 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":"125307440","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

A fusion approach for anomaly detection in hard disk drives 一种硬盘异常检测的融合方法

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228814

Yu Wang, E. W. M. Ma, K. Tsui, M. Pecht

引用次数: 3

Scheduling predictive maintenance in flow-shop flow-shop中的预测性维护调度

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228964

C. Varnier, Noureddine Zerhouni

引用次数: 15

Disease surveillance by clustering based on minimal internal distance 基于最小内距离聚类的疾病监测

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228842

Junjie Cao, K. B. P. Kris Baoqian Pan, K. Tsui, S. Wong

引用次数: 0

Parameter mapping method for Large Commercial Aircraft PHM system joint design 大型商用飞机PHM系统接头设计的参数映射方法

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228932

Rong Wan, Linlong Ma, Tianlun Yuan

{"title":"Parameter mapping method for Large Commercial Aircraft PHM system joint design","authors":"Rong Wan, Linlong Ma, Tianlun Yuan","doi":"10.1109/PHM.2012.6228932","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228932","url":null,"abstract":"During the Large Commercial Aircraft development process through the form of “Manufacturer-Supplier” model, the aircraft design work is allocated to multiple systems and sub-systems down to single equipment and component. This leads to the challenges in the joint design of aircraft PHM system. It is particularly difficult to coordinate between the data and model. Considering the real needs in the optimization of the aircraft PHM system design, this paper describes the establishment of the parameter coupling model between the PHM system and the contributing system (including the sub-system, equipment and components) through the concept of integration. Meanwhile on the basis of the model, the parameter mapping (including parameter identification, demonstration and transfer) among the PHM system and each contributing system, sub-system, equipment and components can be realized through the object-oriented method, so that the effective control of the data collection, transfer, calculation and storage can be achieved. The significant benefit in ensuring sufficient coordination, increasing system design efficiency and quality during the aircraft PHM system joint design is verified in this paper through the investigation of real-world application.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"104 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":"123610817","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

Uncertainty processing in prognostics and health management: An overview 预后和健康管理中的不确定性处理:综述

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228860

Datong Liu, Yue Luo, Yu Peng

引用次数: 7

Study on fault detection using wavelet packet and SOM neural network 基于小波包和SOM神经网络的故障检测研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228817

Xiaochuang Tao, Zili Wang, Jian Ma, Huanzhen Fan

引用次数: 10

Gear crack level classification based on multinomial logit model and cumulative link model 基于多项logit模型和累积环节模型的齿轮裂纹等级分类

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228904

Yizhen Hai, K. Tsui, M. Zuo

引用次数: 9

Research on PHM architecture design with electronic equipment 基于电子设备的PHM体系结构设计研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228813

Yizhou He, Lin Ma

引用次数: 5

Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression 基于高斯过程回归的锂离子电池数据驱动预测

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI: 10.1109/PHM.2012.6228848

Datong Liu, Jingyue Pang, Jianbao Zhou, Yu Peng

{"title":"Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression","authors":"Datong Liu, Jingyue Pang, Jianbao Zhou, Yu Peng","doi":"10.1109/PHM.2012.6228848","DOIUrl":"https://doi.org/10.1109/PHM.2012.6228848","url":null,"abstract":"Lithium-ion battery is a promising power source for electric vehicles owing to its high specific energy and power. Through monitoring battery health in effective way such as determining the operating conditions, planning replacement interval could increase the reliability and stability of the whole system. However, due to the reliance on integration, errors in terminal measurement caused by noise, resolution, the uncertainty when we make prognostics for battery health are cumulative, the prediction result is combined with unsatisfied errors. As a result, the prognostic algorithms supporting uncertainty representation and management are emphasized. So in this paper, we present the Gaussian process model to realize the prognostics for battery health. Because of the advantages of flexible, probabilistic, nonparametric model with uncertainty predictions, the Gaussian process model can provide variance around its mean predictions to describe associated uncertainty in the evaluation and prediction. To evaluate the proposed prediction approach, we have executed experiments with lithium-ion battery. Experimental results prove its effectiveness and confirm the algorithm can be effectively applied to the battery monitoring and prognostics. Furthermore, the comparison of prediction with different amounts of training data has been achieved, and the dynamic model is introduced to improve the prediction for the battery health.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"88 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":"132067327","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: 66