2014 International Conference on Prognostics and Health Management最新文献

Rolling element bearing fault detection using density-based clustering 基于密度聚类的滚动轴承故障检测

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036387

Jing Tian, M. Azarian, M. Pecht

{"title":"Rolling element bearing fault detection using density-based clustering","authors":"Jing Tian, M. Azarian, M. Pecht","doi":"10.1109/ICPHM.2014.7036387","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036387","url":null,"abstract":"Fault detection is a critical task in condition-based maintenance of rolling element bearings. In many applications unsupervised learning techniques are preferred in fault detection due to the lack of training data. Unsupervised learning techniques such as k-means clustering are most widely used in machinery health monitoring. These methods face two challenges: firstly, they cannot cluster non-convex data, which may have arbitrary shape; secondly, no rule has been established for these techniques to find a fault threshold. This paper introduces a fault detection methodology based on density clustering to address these challenges. This methodology assumes that data from healthy bearings is located in regions with a high density and data from faulty bearings is located in low density regions. By finding boundaries of these regions, which may be non-convex, data from faulty bearings can be identified. In this paper the value of the density for healthy bearings and faulty bearings is evaluated. The rate of change of the density from healthy to faulty is identified as a fault threshold. The methodology is validated by experimental data. This methodology can be applied to applications where faulty data are too difficult or costly to acquire. Also it can be used in applications where fault thresholds are difficult to determine.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019972","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 method for dependency matrix combination based on port connection relationship 基于端口连接关系的依赖矩阵组合方法

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036372

Long Chen, Junyou Shi

{"title":"A method for dependency matrix combination based on port connection relationship","authors":"Long Chen, Junyou Shi","doi":"10.1109/ICPHM.2014.7036372","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036372","url":null,"abstract":"Dependency matrix is a kind of description form of the dependency relationships between the faults and tests in a UUT (Unit Under Test). Testability analysis, testability prediction and fault diagnosis can be accomplished on the basis of dependency matrix. The traditional method of acquiring dependency matrix is to search the signal flow of the UUT's dependency graphical model. The difficulties in integrating the models together and the exponentially growing workload to search the signal flow are two major disadvantages of the traditional method. To overcome these disadvantages, a new method for dependency matrix combination based on port connection relationship is proposed. Definitions of port-extended dependency matrix and global port connection matrix are given. The principle of dependency matrix combination method is analyzed with the definitions of the two matrixes above given. The generation algorithms of a port-extended dependency matrix and a global port connection matrix are studied. Based on the port-extended dependency and the global port connection matrix, the algorithm flow of the dependency matrix combination method is given. A case of an avionic system is given and the above method is applied. The result shows that the method is feasible and effective.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694387","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

Computational algorithm for dynamic hybrid Bayesian network in on-line system health management applications 动态混合贝叶斯网络在在线系统健康管理中的应用

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036384

C. Iamsumang

引用次数: 9

Improved multi-kernel LS-SVR for time series online prediction with incremental learning 基于增量学习的改进多核LS-SVR时间序列在线预测

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036376

Yangming Guo, Xiangtao Wang, Yafei Zheng, Chong Liu

引用次数: 0

Robust multivariate statistical ensembles for bearing fault detection and identification 用于轴承故障检测和识别的鲁棒多元统计集成

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036379

Jamie L. Godwin, Peter C. Matthews, Christopher Watson

{"title":"Robust multivariate statistical ensembles for bearing fault detection and identification","authors":"Jamie L. Godwin, Peter C. Matthews, Christopher Watson","doi":"10.1109/ICPHM.2014.7036379","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036379","url":null,"abstract":"This paper presents a novel methodology for the identification and detection of faults on based upon high frequency bearing data collected from a 6205-2RS JEM SKF bearing. A robust derivative of the Mahalanobis distance is employed to accurately and precisely encapsulate varying fault behaviours, which can then be exploited for the purposes of fault detection and identification. Domain knowledge in the form of failure mode and effect analysis (FMEA) can be incorporated into the model, to determine potential failure modes. Seeded fault data was employed to derive the shape and location estimates to enable the use of a multivariate distance function. To reduce the computational complexity whilst simultaneously increasing sensitivity to the faults, the high frequency (48KHz) accelerometer data was pre-processed into a 4-tuple consisting of the Skewness, Kurtosis, Root mean square (RMS) and Shannon Entropy. This 4-tuple is shown to encapsulate and discriminate all fault modes identified through the FMEA, whilst reducing the data to 1Hz, allowing for the both exact, and meta-heuristic algorithms to be employed for robust analysis. Sensitivity to minimal fault development is demonstrated, with the technique accurately identifying 0.007\" diameter inner race, outer race and roller element faults which had been seeded to the bearing through electro-discharge machining. To demonstrate the practicalities of the approach, the trained system is employed for analysis of an independent dataset, collected under different conditions. The technique is shown to accurately detect and identify the relevant fault mode pre-emptively, before catastrophic failure occurred, with 28.6% of bearing life remaining.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726768","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: 6

New algorithms for diagnosing defects of an air-operated valve for self diagnostic monitoring system 气动阀自诊断监测系统缺陷诊断新算法

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036398

Wooshik Kim, Jangbom Chai

引用次数: 3

A new hybrid hierarchy model description method 一种新的混合层次模型描述方法

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036370

Qi Zhao, Wenfeng Zhang, Gan Zhou, XiuMei Guan

{"title":"A new hybrid hierarchy model description method","authors":"Qi Zhao, Wenfeng Zhang, Gan Zhou, XiuMei Guan","doi":"10.1109/ICPHM.2014.7036370","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036370","url":null,"abstract":"As requirements in diagnosis for hybrid systems increase, more and more researchers concentrate on hybrid models. However, common visual modeling methods such as GME (General Modeling Environment) lacks of flexibility. There is no appropriate modeling method for hybrid systems in cases containing plenty of complex components. This paper proposes a new hybrid hierarchy model description method, LLSM (Language for Large-Scale Modeling), based on concurrent probabilistic hybrid automata (cPHA) to make the process expediently. LLSM describes systems in the form of text. It settles the problem in three aspects: granularity, hierarchy and reusability. Component-oriented modeling of LLSM helps control granularity easily allowing users to create models in different scales. A special mark, which is employed to represent hierarchical relationship makes the system clearer and guides the accuracy of diagnosis. Reusability is achieved by C-style grammar which indicates component libraries for large-scale applications. In complex applications, LLSM creates models efficiently by existing libraries in the form of collaboration. Test on a switch demonstrates how it works.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124682049","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

Novel RUL prediction of assets based on the integration of auto-regressive models and an RUSBoost classifier 基于自回归模型和RUSBoost分类器集成的新型资产RUL预测

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036373

G. Fagogenis, D. Flynn, D. Lane

引用次数: 7

Comparison of resampling algorithms for particle filter based remaining useful life estimation 基于剩余使用寿命估计的粒子滤波重采样算法比较

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036395

Limeng Guo, Yu Peng, Datong Liu, Yue Luo

{"title":"Comparison of resampling algorithms for particle filter based remaining useful life estimation","authors":"Limeng Guo, Yu Peng, Datong Liu, Yue Luo","doi":"10.1109/ICPHM.2014.7036395","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036395","url":null,"abstract":"Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271059","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: 13

Prognostics model for tool life prediction in milling using texture features of surface image data 基于表面图像数据纹理特征的铣削刀具寿命预测模型

2014 International Conference on Prognostics and Health Management Pub Date : 2014-06-22 DOI: 10.1109/ICPHM.2014.7036383

K. Kumar, N. Arunachalam, L. Vijayaraghavan

{"title":"Prognostics model for tool life prediction in milling using texture features of surface image data","authors":"K. Kumar, N. Arunachalam, L. Vijayaraghavan","doi":"10.1109/ICPHM.2014.7036383","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036383","url":null,"abstract":"In a machine tool, the cutting tool is mainly responsible for producing a component with good surface quality. With the time the cutting tool wear out and affects the surface quality. Hence it is very important to monitor the condition of the cutting tool to avoid the production of substandard parts. In this work the face milling cutter is made to interact with hardened steel components to manufacture the required surfaces with a specified amount of stock removal. The cutting conditions are selected and machining is done till the tool reaches its critical flank wear value. The captured surface images are analyzed using the statistical and spectral texture analysis methods. The flank wear of the cutting insert is measured at frequent intervals. The evaluated texture features are correlated with the flank wear using the multivariate correlation methods. The significant features are selected based on the correlation value and its mutual correlation value with other features. The selected texture features are plotted against machining time or the number of components. The developed regression model based on the selected parameters and the time is used to predict the flank wear.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294286","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