2013 IEEE Conference on Prognostics and Health Management (PHM)最新文献_第4页

Prognostic health monitoring for a micro-coil spring interconnect subjected to drop impacts 受跌落冲击的微线圈弹簧互连的预估健康监测

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621458

P. Lall, Ryan Lowe, K. Goebel

{"title":"Prognostic health monitoring for a micro-coil spring interconnect subjected to drop impacts","authors":"P. Lall, Ryan Lowe, K. Goebel","doi":"10.1109/ICPHM.2013.6621458","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621458","url":null,"abstract":"In an effort to meet reliability requirements for long term human presence in space without the need for resupply, a new interconnect for grid array packages has been developed. The interconnect utilizes beryllium copper springs which are 0.05 inches in height as interconnects between the package and PCB. These novel interconnects are known as micro coil springs (MCS). The configuration is approximately the same height as copper column interconnects, but has increased compliance compared to traditional column interconnects. Because the interconnect is still in the design stage, the feasibility of integrating prognostic health management capability into the interconnect is being studied. Failure prognostics, or the prediction of impending failure for individual components, would help ensure the reliability of systems deployed on long duration space missions and provide warnings of potential failure with adequate time to formulate contingency plans. Prognostic monitoring circuitry, prediction algorithms, and performance validation are discussed for micro coil packages subjected to JDEC standard drop testing.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128844968","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: 4

Multiple model particle filtering for bearing life prognosis 轴承寿命预测的多模型粒子滤波

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621423

Jinjiang Wang, R. Gao

引用次数: 13

Health diagnostics of water-cooled power generator stator windings using a Directional Mahalanobis Distance (DMD) 基于定向马氏距离(DMD)的水冷发电机定子绕组健康诊断

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621439

K. Park, B. Youn, J. Yoon, Chao Hu, H. Kim, Y. Bae

引用次数: 1

A cloud-based approach for smart facilities management 基于云的智能设施管理方法

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621459

D. Lau, J. Liu, S. Majumdar, B. Nandy, M. St-Hilaire, C. S. Yang

引用次数: 18

Machinery time to failure prediction - Case study and lesson learned for a spindle bearing application 机械故障时间预测。主轴轴承应用的案例研究和经验教训

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621416

Linxia Liao, Radu Pavel

{"title":"Machinery time to failure prediction - Case study and lesson learned for a spindle bearing application","authors":"Linxia Liao, Radu Pavel","doi":"10.1109/ICPHM.2013.6621416","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621416","url":null,"abstract":"One of the important roles of prognostics health management (PHM) is to predict the time to failure of a system in order to avoid unexpected downtime and optimize maintenance activities. Although many attempts to predict time to failure have been reported in the literature, there are still challenges related to data availability and methodology. In addition, there is significant variation from case to case due to complexity of system usage and failure modes. This paper reveals various aspects related to such challenges experienced while applying a novel predictive technology to a spindle test-bed. The goal was to evaluate the ability of the technology to predict the remaining useful life of a bearing with seeded faults. Testing has been conducted to reveal the effectiveness of signal processing, health modeling and prediction techniques. While conducting the evaluation tests, besides some well-known bearing failure modes, an unusual case was recorded. This atypical bearing failure mode created a new challenge for the predictive technology being investigated, which prompted the development of an advanced feature discovering methodology using genetic programming. This new methodology and the technology evaluation results obtained for both the well-known and the atypical failure modes will be discussed in the paper. In addition, the paper will describe the test-bed and instrumentation approach, the data acquisition system and the experimental design for testing and validation of the technology.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"141 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130924512","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: 10

Prognostics-based product warranties 基于预测的产品保证

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621436

Yan Ning, P. Sandborn, M. Pecht

引用次数: 5

Vibration-based robust health diagnostics for mechanical failure modes of power transformers 基于振动的电力变压器机械故障模式鲁棒健康诊断

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621421

J. Yoon, B. Youn, K. Park, Wook-ryun Lee

引用次数: 8

Test point selection based on functional simulation and FMMEA for an electronic system on PHM 基于功能仿真和FMMEA的PHM电子系统测试点选择

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621444

Xufei Wang, Zhongqun Li, Shunong Zhang, Jiaming Liu, Cong Shao

引用次数: 5

Rolling element bearing fault diagnosis using simulated annealing optimized spectral kurtosis 基于模拟退火优化谱峰度的滚动轴承故障诊断

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621440

Jing Tian, C. Morillo, M. Pecht

{"title":"Rolling element bearing fault diagnosis using simulated annealing optimized spectral kurtosis","authors":"Jing Tian, C. Morillo, M. Pecht","doi":"10.1109/ICPHM.2013.6621440","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621440","url":null,"abstract":"To diagnose the bearing fault using vibration signal, methods like envelope analysis have been used. These methods need to locate the optimum frequency band to perform the analysis. Researchers have developed spectral kurtosis through kurtogram to detect the optimum frequency band. However, kurtogram uses a rigid structure of frequency filter bank and when the optimum frequency band does not match any of the frequency bands in the structure the fault may not be detected. In this paper a method based on simulated annealing is developed to locate the optimum frequency band. The method models spectral kurtosis as a function of the variables of a band-pass filter. Firstly the analysis result from the kurtogram is obtained as a start point, and then the central frequency and the bandwidth are optimized by maximizing spectral kurtosis through simulated annealing. Finally, the test signal is band-pass filtered by the optimized filter, and the envelope analysis is applied to complete the diagnosis. Experimental study shows that the method can diagnose the fault for different fault types. Being able to detect the real optimum frequency band, this method can strengthen the detection of the fault feature frequency component.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125679154","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: 27

Incremental learning approach for improved prediction 改进预测的增量学习方法

2013 IEEE Conference on Prognostics and Health Management (PHM) Pub Date : 2013-06-24 DOI: 10.1109/ICPHM.2013.6621433

Chao-Shiou Chen, S. Kunche, M. Pecht

{"title":"Incremental learning approach for improved prediction","authors":"Chao-Shiou Chen, S. Kunche, M. Pecht","doi":"10.1109/ICPHM.2013.6621433","DOIUrl":"https://doi.org/10.1109/ICPHM.2013.6621433","url":null,"abstract":"Prognostics is the key function in prognostics and health management (PHM), which can provide remaining useful life of systems in real-time so that timely maintenance plans can be scheduled to avoid system downtime and even catastrophic events. In system prognostics, fault degradation models are necessarily established to describe the fault evolution dynamics and used to extrapolate the future health conditions. However, it is very challenging to build an accurate fault degradation model considering the complex fault growth dynamics and numerous modeling uncertainties, such as unit to unit variation. Particularly, in data driven modeling methods, the variations of loading conditions, environments and usage patterns will influence greatly the fault modeling accuracy. Some research has been conducted to tackle this problem by utilizing real-time monitoring data to update the fault model in terms of model parameters and even model structures to accommodate these varying factors. But whenever new data are available, it becomes difficult to determine how to retain the prior learned model while also learning new fault degradation dynamics. That is, how to learn new knowledge without forgetting what was learned previously. In this paper, we develop a new model update and fusion method for prognostics by using incremental learning. A case study is given to validate the developed approach via the battery degradation data.","PeriodicalId":178906,"journal":{"name":"2013 IEEE Conference on Prognostics and Health Management (PHM)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148331","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