2019 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第7页

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Multi-model Gaussian Process 基于多模型高斯过程的锂离子电池剩余使用寿命预测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819384

Meng Li, Mohammadkazem Sadoughi, Sheng Shen, Chao Hu

{"title":"Remaining Useful Life Prediction of Lithium-Ion Batteries Using Multi-model Gaussian Process","authors":"Meng Li, Mohammadkazem Sadoughi, Sheng Shen, Chao Hu","doi":"10.1109/ICPHM.2019.8819384","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819384","url":null,"abstract":"This paper presents a multi-model Gaussian process (MMGP) approach for predicting the RUL of lithium-ion batteries. The proposed MMGP approach incorporates multiple candidate capacity fade models, as the trend functions of a Gaussian process (GP) model, to capture the multi-stage capacity fade trend of the batteries. First, the hypothetical capacities at a predefined number of future cycles are predicted at the current cycle based on the offline data using similarity-based extrapolation. Then, the active fade model is selected by comparing the hypothetical capacities with the GP model-projected capacities using each candidate fade model and the active model is employed as the trend function of the GP model. Finally, the distribution of the RUL is estimated by determining when the projected capacity curves using the GP model down-cross a pre-defined capacity threshold. The MMGP approach was used for the RUL prediction of eight lithiumion battery cells that show multi-stage capacity fade behavior when cycled with a daily current rate (i.e., C/24). The capacities of these cells initially degrade rapidly, followed by a reduced fade rate and then a faster linear fade rate. The RUL prediction results suggest that the proposed MMGP approach can adaptively select proper trend functions for the GP model at different capacity fade stages throughout the lifetime, as well as adapting the models to accommodate variations in the capacity fade performance among different cells.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607755","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

NDE 4.0: Smart NDE

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819429

Debejyo Chakraborty, Megan E. McGovern

引用次数: 10

ICPHM 2019 Copyright Page ICPHM 2019版权页面

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/icphm.2019.8819411

引用次数: 0

Online change detection techniques in time series: An overview 时间序列中的在线变更检测技术:概述

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819394

Bernadin Namoano, A. Starr, C. Emmanouilidis, C. Ruiz-Carcel

引用次数: 7

Differentiation of Journal Bearing Friction States under varying Oil Viscosities based on Acoustic Emission Signals 基于声发射信号的不同油粘度下滑动轴承摩擦状态的判别

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819371

N. Mokhtari, René Knoblich, S. Nowoisky, J. Bote-Garcia, C. Gühmann

{"title":"Differentiation of Journal Bearing Friction States under varying Oil Viscosities based on Acoustic Emission Signals","authors":"N. Mokhtari, René Knoblich, S. Nowoisky, J. Bote-Garcia, C. Gühmann","doi":"10.1109/ICPHM.2019.8819371","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819371","url":null,"abstract":"For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An integral component for this is the determination of the bearing friction condition. Hydrodynamic journal bearings experience three basic types of friction states: fluid, mixed and solid friction, whereas the last two types cause mechanical wear.This paper deals with the differentiation of these three basic types of journal bearing friction conditions under several rotational speed, load and oil viscosity combinations based on acoustic emission (AE) signals. The aim of this work is to show that it is possible to detect various oil viscosities under same loads and rotational speeds with AE features. An already developed classifier [1], which is trained and tested under various rotational speed and load combinations, can then be improved by training and testing it under several oil viscosities.Different oil viscosities were generated by varying the oil temperature. A special test environment is introduced for this purpose. The actual friction state was verified by the contact voltage (CV) between shaft and bearing [2].","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129195202","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

ICPHM 2019 Program ICPHM 2019计划

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/icphm.2019.8819398

引用次数: 0

A Comparative Study of Deep Learning-Based Diagnostics for Automotive Safety Components Using a Raspberry Pi 基于深度学习的汽车安全部件诊断的比较研究

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819436

Namkyoung Lee, M. Azarian, M. Pecht, Jinyong Kim, Jongsoon Im

引用次数: 3

Fault Recognition Technology for Pipeline Systems Based on Multi-feature Fusion of Monitoring Data 基于监测数据多特征融合的管道系统故障识别技术

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819415

Hongquan Jiang, Jianmin Gao, Fengshe Xia, Xiaoming Zhang, T. Zhou, Dongcheng Liu

{"title":"Fault Recognition Technology for Pipeline Systems Based on Multi-feature Fusion of Monitoring Data","authors":"Hongquan Jiang, Jianmin Gao, Fengshe Xia, Xiaoming Zhang, T. Zhou, Dongcheng Liu","doi":"10.1109/ICPHM.2019.8819415","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819415","url":null,"abstract":"Pipeline systems are important parts of modern infrastructure and complex industrial systems. Effective fault identification for pipeline systems is of great significance in ensuring safety and reliability. In this work, a method for pipeline fault identification based on data fusion analysis is proposed, which utilizes pipeline system condition monitoring data. First, monitoring data are analyzed to extract features that represent the operating state of the pipeline system, and a feature evaluation index based on sensitivity and volatility is proposed to optimize the extracted features. Second, multi-feature fusion analysis based on the Dempster–Shafer (DS) theory is performed to identify faults within the pipeline system. Meanwhile, to solve the problem of obtaining the basic probability assignment function (BPA) in DS theory, a BPA acquisition method is proposed, which considers both distance and correlation. Finally, this work is validated using case data of a residential heating test platform. The results show that the proposed method can directly use the monitoring data from the pipeline system for fault analysis and can effectively identify pipeline leakages, blockages, and other faults. The proposed method overcomes the shortcomings of traditional methods, which require a detailed mechanism analysis. It also conforms to emerging technology development trends, which utilize and apply Big Data analysis.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738855","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

An Adaptive Fault Diagnosis System Framework for Aircraft Based on Man-in-loop 基于人在环的飞机自适应故障诊断系统框架

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819430

Lu Yang, Jian Wang, Guigang Zhang, Xingwang Li, Haiyan Fu

引用次数: 2

Estimating remaining useful life of machine tool ball screws via probabilistic classification 用概率分类法估计机床滚珠丝杠的剩余使用寿命

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819445

Maximilian Benker, Robin Kleinwort, M. Zäh

{"title":"Estimating remaining useful life of machine tool ball screws via probabilistic classification","authors":"Maximilian Benker, Robin Kleinwort, M. Zäh","doi":"10.1109/ICPHM.2019.8819445","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819445","url":null,"abstract":"Ball screws are key components in machine tool linear feed drives since they translate the motors’ rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw’s preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"629 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015086","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