2016 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第4页

A lithium-ion battery RUL prognosis method using temperature changing rate 基于温度变化率的锂离子电池RUL预测方法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542866

Li Yang, Lingling Zhao, Xiaohong Su, Shuai Wang

{"title":"A lithium-ion battery RUL prognosis method using temperature changing rate","authors":"Li Yang, Lingling Zhao, Xiaohong Su, Shuai Wang","doi":"10.1109/ICPHM.2016.7542866","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542866","url":null,"abstract":"As a kind of complex electrochemical system, the performance of lithium-ion battery will degrade under continuous charging and discharging. It's particularly crucial to monitor the battery state of health and prognosis the battery remaining useful life (RUL). Considering the highly linear correlation between capacity and the changing rate of temperature (TR), a new RUL prediction approach is proposed which provides a better description on the capacity degradation based on the changing rate of battery temperature and cycle number N. First a binary linear regress model is proposed for battery state of health (SOH) and RUL prognosis. Then TR ratio which is extracted for TR prediction is predicted using the chosen historical data considering the similarity of different data sets. Finally, capacity is estimated sequentially based on the proposed model with the predicted TR and cycle number N. The results show that TR can not only indicate state-of-health more accurately, but also provide more precise and better robustness in RUL prediction and SOH monitoring. Furthermore, the regeneration of battery can be accurately predicted by our method.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115203366","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: 11

A modified Mahalanobis-Taguchi System analysis for monitoring of ball screw health assessment 滚珠丝杠健康监测的改进马氏-田口系统分析

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542861

Shuai Zhao, Yixiang Huang, Haoren Wang, Chengliang Liu, Yanming Li, Xiao Liu

{"title":"A modified Mahalanobis-Taguchi System analysis for monitoring of ball screw health assessment","authors":"Shuai Zhao, Yixiang Huang, Haoren Wang, Chengliang Liu, Yanming Li, Xiao Liu","doi":"10.1109/ICPHM.2016.7542861","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542861","url":null,"abstract":"The ball screw's health assessment is significant to keep accuracy and reliability of the motion axes in the CNC machine. Mahalanobis-Taguchi System (MTS) is considered to be an effective non-parametric approach to carry out the health assessment. In this paper, a Laplacian Mahalanobis-Taguchi system (referred as LMTS) analytical model is proposed to establish a nonlinear mapping relationship between the features of sensor information and the ball screw performance. In order to utilize the limited sensor data effectively, LMTS method is only performed on the speed and motor current signals which are available in CNC secondary-develop interface. Because of the complexity of processing high dimensionality nonlinear features, Laplacian Eigenmaps is utilized to reduce the feature data dimension before they were sent to Mahalanobis-Taguchi System as inputs. Compared with the classical dimension reduction methods, the intrinsic low dimensionality manifold by Laplacian Eigenmaps in Mahalanobis feature space characterizes the performance degradation more accurately and robustly. Among many ball screw assessment technologies, this LMTS assessment is a promising data driven based approach because of less influence in the machining process and few changes in the original structural design. The results show that LMTS monitoring may enable the practical application of online real-time assessment for ball screws.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115716796","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: 9

Model-based method for estimating LiCoO2 battery state of health and behaviors 基于模型的锂离子电池健康状态和行为估计方法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542819

Junfu Li, Chao Lyu, Lixin Wang, Tengfei Ge

引用次数: 2

SOH estimation for lithium-ion batteries: A cointegration and error correction approach 锂离子电池SOH估计:协整和误差校正方法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542828

Chen Kunlong, Jiang Jiuchun, Zheng Fangdan, Sun Bing-xiang, Zhang Yanru

引用次数: 8

i-RCAM: Intelligent expert system for root cause analysis in maintenance decision making i-RCAM:用于维修决策根本原因分析的智能专家系统

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542830

P. Chemweno, L. Pintelon, Lara's Jongers, P. Muchiri

{"title":"i-RCAM: Intelligent expert system for root cause analysis in maintenance decision making","authors":"P. Chemweno, L. Pintelon, Lara's Jongers, P. Muchiri","doi":"10.1109/ICPHM.2016.7542830","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542830","url":null,"abstract":"The increasing adoption of maintenance information management systems for maintenance decision support by industry has facilitated the collection of large volumes of maintenance data. Apart from enhancing maintenance decision support in aspects such as task planning or resource allocation, the data could assist decision makers identify the focal root causes of recurrent equipment failures. In this way, more effective strategies may be formulated and targeted at these focal causes. Despite the increased adoption of maintenance information systems and, as such, availability of maintenance data few techniques so far developed leverage on the maintenance data for decision support in root cause analysis. A particular focus in this regard relates to application of techniques for data mining such as association rule mining. In particular, association rule mining is attractive in the sense of analyzing failure associations embedded in the maintenance data. Thus, this study proposes a methodology for enhancing decision support for root cause analysis in maintenance decision making. The methodology leverages on two association rule mining algorithms - Apriori and Predictive Apriori. Moreover, the methodology incorporates a data standardization step, whereof standard terms and vocabulary are adopted from the ISO 14224 and used for standardizing the equipment failure descriptions. Thereafter, the standardized descriptions are applied as input to an association rule mining framework from which important failure associations are extracted and validated by experts for relevancy. After which, the extracted failure associations are used to generate causal maps, and from the maps, the focal root causes of the equipment failure are identified. The added value of the proposed methodology is demonstrated in the application case of thermal power plant maintenance data.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126057965","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: 14

Diagnosing wind turbine faults using machine learning techniques applied to operational data 使用应用于运行数据的机器学习技术诊断风力涡轮机故障

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542860

K. Leahy, R. Hu, Ioannis C. Konstantakopoulos, C. Spanos, A. Agogino

{"title":"Diagnosing wind turbine faults using machine learning techniques applied to operational data","authors":"K. Leahy, R. Hu, Ioannis C. Konstantakopoulos, C. Spanos, A. Agogino","doi":"10.1109/ICPHM.2016.7542860","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542860","url":null,"abstract":"Unscheduled or reactive maintenance on wind turbines due to component failures incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. By continuously monitoring turbine health, it is possible to detect incipient faults and schedule maintenance as needed, negating the need for unnecessary periodic checks. To date, a strong effort has been applied to developing Condition monitoring systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, data is obtained from the SCADA system of a turbine in the South-East of Ireland. Fault and alarm data is filtered and analysed in conjunction with the power curve to identify periods of nominal and fault operation. Classification techniques are then applied to recognise fault and fault-free operation by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show success in predicting some types of faults.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131274336","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: 81

Predicting the lifetimes of LiFePO4 batteries on the basis of the gamma process through accelerated degradation measurements 基于伽马过程，通过加速降解测量来预测LiFePO4电池的寿命

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542849

Yu-Chang Lin, K. Chung

{"title":"Predicting the lifetimes of LiFePO4 batteries on the basis of the gamma process through accelerated degradation measurements","authors":"Yu-Chang Lin, K. Chung","doi":"10.1109/ICPHM.2016.7542849","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542849","url":null,"abstract":"This study mainly focused on evaluating the capacity fade of LiFePO4 batteries by using a novel dual dynamic stress accelerated degradation test, called D2SADT. This test method was developed to simulate a situation involving driving an electric vehicle in the city. D2SADT contains two controllable dynamic stress variables: the environmental factor corresponding to temperature cycling and the power factor corresponding to charging-discharging currents and times at which they were implemented simultaneously. A reference power test was performed repeatedly at a certain time (e.g., five temperature cycles), and the cell capacity was then calculated to monitor the degradation of the batteries. A compositional reliability assessment using the gamma process and Monte Carlo simulation was implemented to calculate the likelihood values of the test samples, LiFePO4 batteries, on the basis of their capacity loss. The test results indicate that the battery capacity decreases over time, validating the novel test method (D2SADT). Moreover, the modeling results indicate that the gamma process combined with Monte Carlo simulation provide superior accuracy for predicting the lifetimes of the test batteries compared with the baseline lifetime data (true degradation route and lifetime). Furthermore, the results indicate the high prediction performance of the proposed model because an error rate of within 5% was obtained after half of the cycles were completed (70 temperature cycles), including the measurements.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891512","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

Online sequential extreme learning machines for fault detection 用于故障检测的在线顺序极限学习机

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542841

Yang Hu, Olga Fink, Thomas Palmé

引用次数: 5

On-line reliability assessment for an electronic system subject to condition monitoring 状态监测电子系统在线可靠性评估

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542817

Shuai Zhao, V. Makis, Shaowei Chen, Yong Li

引用次数: 8

Battery remaining useful life prediction algorithm based on support vector regression and unscented particle filter 基于支持向量回归和无气味粒子滤波的电池剩余使用寿命预测算法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542844

Xi Peng, Chao Zhang, Yang Yu, Yong Zhou

引用次数: 10