2016 Prognostics and System Health Management Conference (PHM-Chengdu)最新文献_第7页

Fatigue life prediction for structure under fatigue and low-energy impact based on continuum damage mechanics 基于连续损伤力学的疲劳低能冲击下结构疲劳寿命预测

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819820

Yi Jin, Yunxia Chen, R. Kang, Yahui Li

{"title":"Fatigue life prediction for structure under fatigue and low-energy impact based on continuum damage mechanics","authors":"Yi Jin, Yunxia Chen, R. Kang, Yahui Li","doi":"10.1109/PHM.2016.7819820","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819820","url":null,"abstract":"A product characterized by fatigue-induced failure is subjected to complex cyclic load which cause fatigue damage accumulated over time. At the same time, the product under cyclic load may suffer from low-energy impact during the whole service life. For its discontinuity, the low-energy impact does not lead to the product failure immediately. The low-energy impact not only cause structural damage and the residual strength degradation instantly but also influence the effect of cyclic load. And cyclic load has the same effect for structural in return. This article shows an approach for fatigue life prediction based on continuum damage mechanics with coupling relationship between fatigue damage and impact damage. First, the features and coupling relationships of fatigue damage and impact damage are discussed in this essay and both of them result in a drop in residual strength of structure. Second, the initial stress response under impact and fatigue loads were obtained. The existed evolution equations of fatigue and impact damage were respectively introduced. Third, based on these damage evolution equations, a new coupling damage model for fatigue damage and impact damage has been established and the damage and residual strength evolution of the structure is proposed. Finally, an engineering case is introduced to validate the approach.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128805153","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

Service reliability modeling of the IT infrastructure of active-active cloud data center 双活云数据中心IT基础架构服务可靠性建模

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819903

Yue Liu, Xiaoyang Li, R. Kang, Lianghua Xiao

{"title":"Service reliability modeling of the IT infrastructure of active-active cloud data center","authors":"Yue Liu, Xiaoyang Li, R. Kang, Lianghua Xiao","doi":"10.1109/PHM.2016.7819903","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819903","url":null,"abstract":"With the increasing use in different areas, cloud data center has gradually showed its superiority in availability, resource utilization and disaster recovery during the service delivery, compared with traditional data center. According to SLA (Service-Level Agreement), the demand on service reliability and other related indexes are put forward. Despite efforts at fault tolerance and redundancy, the occurrence of failure in data center is still inevitable. Hence, there is a need to model and analyze the service reliability of data center. However, traditional method of reliability modeling is no longer applicable because of the complicated cloud control flows, massive-scale service sharing, and complexity real-word infrastructures. This paper proposes a new approach to model the service reliability of the IT infrastructure of active-active data center, which is a typical form of cloud data center. Firstly, we divide the process of service delivery into two stages — the request stage and execution stage. Then, two models are built for two stages, respectively. For request stage, we use the Queuing Theory and Monte Carlo Method while for execution stage, the Graph Theory and Monte Carlo Method are adopted. Based on the proposed model, the service reliability of data center can be calculated. With the data of a company active-active data center as a case study, we finally demonstrate the applicability and correctness of the model.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122217801","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

Research on heading sensitive drift storage behavior of inertial platform system under the action of assembly stress relaxation 装配应力松弛作用下惯性平台系统航向敏感漂移存储行为研究

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819759

Guoqing Zha, Xiaokai Huang, Wen-Jung Liu

{"title":"Research on heading sensitive drift storage behavior of inertial platform system under the action of assembly stress relaxation","authors":"Guoqing Zha, Xiaokai Huang, Wen-Jung Liu","doi":"10.1109/PHM.2016.7819759","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819759","url":null,"abstract":"In this paper, firstly, the relaxation mechanism and change law of the assembly stress of inertial platform and bearing assembly structure was thoroughly analyzed, theoretical model was derived and identified by experiment. Secondly, the change law of bearing contact angle under the action of assembly stress relaxation was obtained. On that basis, the bearing contact stiffness mechanism model and the damping change mechanism model of the assembly structure of inertial platform and bearing were derived. Thirdly, the variation mechanism of inertial platform system vibration transmissibility caused by the contact stiffness and damping mechanism models was thoroughly researched by the kinematic differential equation of inertial platform system, and then heading sensitive drift behavior model of inertial platform system influenced by the contact stiffness and damping of the assembly structure was obtained. Finally, the established heading sensitive drift behavior model was verified by the comparative analysis of actual storage data, and simulation data according to the established model of inertial platform system, and the long-term drift characteristics, acceleration characteristics and storage stability characteristics of heading sensitive drift storage behavior were thoroughly analyzed under the long-term storage condition.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122217926","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 rigid-flexible coupled model for a planetary gearbox with tooth crack and its dynamic response analyses 含齿裂行星齿轮箱刚柔耦合模型及动力响应分析

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819861

Cheng Chen, Wei Guo, Lingjian Huang

{"title":"A rigid-flexible coupled model for a planetary gearbox with tooth crack and its dynamic response analyses","authors":"Cheng Chen, Wei Guo, Lingjian Huang","doi":"10.1109/PHM.2016.7819861","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819861","url":null,"abstract":"A planetary gearbox is widely used in many areas. Considering that it has complicated structures and relative motions, its dynamic responses are much more complex. It is difficult to analyze its internal and real situation when there exist faults on its surface. Many dynamic models have been established to study its dynamic responses when having a crack on one of teeth. However, most of models are built based on the assumption that the crack propagation path is simplified as a straight line, and it results in obvious deviation on the dynamic responses. In this paper, a rigid-flexible coupled model is established for a one-stage planetary gearbox. In this model, the sun is set as a flexible body and the whole planetary gearbox is a rigid object. The tooth crack propagations are simulated along both the tooth width and the crack depth that can be respectively described as two parabolas. Different crack sizes on one gear tooth are simulated in ADAMS and the dynamic responses are then generated. Based on these, five commonly used statistical indicators, i.e. peak-to-peak, mean, root mean square (RMS), kurtosis, and energy are used to investigate the relationship between the different crack sizes and the response. The results show that RMS and energy are more sensitive to the growth of tooth crack for propagation whatever along the tooth width and the crack length. The frequency spectra also indicate that sidebands caused by the tooth crack obviously change with the crack growing. It provides a reference for studying the dynamic response and crack assessment of the planetary gearbox.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785069","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

Modeling and analysis of failure mechanism dependence based on Petri net 基于Petri网的失效机理相关性建模与分析

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819904

Yingyi Li, Ying Chen, Ning Tang, Liu Yang

引用次数: 3

Quantitative description of sensor data monotonic trend for system degradation condition monitoring 系统退化状态监测中传感器数据单调趋势的定量描述

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819924

Liansheng Liu, Shaojun Wang, Datong Liu, Yu Peng

引用次数: 4

Big data oriented root cause identification approach based on PCA and SVM for product infant failure 基于主成分分析和支持向量机的产品故障大数据根本原因识别方法

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819776

Zhenzhen He, Yihai He, Yi Wei

{"title":"Big data oriented root cause identification approach based on PCA and SVM for product infant failure","authors":"Zhenzhen He, Yihai He, Yi Wei","doi":"10.1109/PHM.2016.7819776","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819776","url":null,"abstract":"Due to the increasing complexity and huge number of uncontrolled operational factors in manufacturing, the produced product usually comes with an exceptional high infant failure rate, and the root cause identification of product infant failure has been a very challenging issue for manufacturers. Especially in the era of big data, the large number of data could be collected from the product life cycle easily, these high-dimensional big data always bear many un-correlation noise information, which has caused serious problem that not only the accuracy may not be remarkable, but also the model-training time may be redundant to most of the current small data-driven method. Furthermore, traditional small data oriented analytic techniques are not applicable to the new big data environment. In order to solve this dilemma, this paper proposed a new method to identify the root cause of infant failure from the big data using the principal component analysis (PCA) and support vector machine (SVM). Firstly, data collected from design, manufacturing, and usage related to product infant failure mechanism has been divided into training data and test data. Secondly, PCA is applied to eliminate redundancy and reducing data dimension of original process feature parameters from raw data in low-dimensional space so that the key variables as the potential root cause candidates can be extracted. Thirdly, an SVM-based optimal hyper-plane to separate these candidates' features data is presented, and one-versus-all SVM classifier is designed to identify the final list of the root cause for infant failure by radial basis kernel function. Finally, the feasibility and validity of the proposed methods are demonstrated through a case study of computer board failure analysis.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133701795","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

Prognostics and health management solution development in LabVIEW: Watchdog agent® toolkit and case study 预后和健康管理解决方案的开发LabVIEW: Watchdog agent®工具包和案例研究

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819780

Zhe Shi, J. Lee, Peng Cui

{"title":"Prognostics and health management solution development in LabVIEW: Watchdog agent® toolkit and case study","authors":"Zhe Shi, J. Lee, Peng Cui","doi":"10.1109/PHM.2016.7819780","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819780","url":null,"abstract":"Prognostics and Health Management (PHM) research has been intensively studied in various industries in order to improve the overall performance of the critical assets and avoid unexpected failure. For data-driven approach, many data analysis methods have been applied for PHM system development including digital signal processing, data mining, machine learning and pattern recognition. And three major goals for PHM system, including health assessment, fault diagnosis and remaining useful life prediction, are achieved by combining different algorithms. However, the challenges for industry are how to efficiently select appropriate tools to develop a suitable PHM solution and how to quickly demonstrate PHM concepts for different applications. The concept of a reconfigurable algorithm toolset titled the Watchdog Agent® for PHM was first presented in 2003 and now is a commercialized toolbox in LabVIEW. The Watchdog Agent® toolbox consists of selected tools/algorithms from four categories: signal processing, health assessment, fault diagnosis and remaining useful life prediction. LabVIEW, a system design software developed by National Instruments, has been used for measurement, testing, control and data analytics in various areas including wind energy, automobile manufacturing, aerospace, etc. This paper first presents an introduction about Watchdog Agent® (WDA) Toolkit and then provides a systematic approach for PHM solutions development in LabVIEW environment. A detailed discussion about data acquisition, data pre-processing, feature extraction, model training and result visualization are provided with case studies.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121380207","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

Fault diagnosis techniques for planetary gearboxes under variable conditions: A review 变工况下行星齿轮箱故障诊断技术综述

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819889

Xin Zhang, Lei Wang, Q. Miao

引用次数: 10

A framework for asset prognostics from fleet data 从车队数据进行资产预测的框架

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819824

Jie Liu, E. Zio

引用次数: 4