2017 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第2页

Prognostics of remaining useful life for lithium-ion batteries based on a feature vector selection and relevance vector machine approach 基于特征向量选择和相关向量机方法的锂离子电池剩余使用寿命预测

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998297

Xiaoli Qin, Qi Zhao, Hongbo Zhao, W. Feng, XiuMei Guan

{"title":"Prognostics of remaining useful life for lithium-ion batteries based on a feature vector selection and relevance vector machine approach","authors":"Xiaoli Qin, Qi Zhao, Hongbo Zhao, W. Feng, XiuMei Guan","doi":"10.1109/ICPHM.2017.7998297","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998297","url":null,"abstract":"Although the capacity is often used as a criterion to evaluate the state of health (SOH) of a lithium-ion battery, it cannot be measured on-line. Besides, degradation modeling only depending on historical capacity data will cause a large prediction error in the long term. Actually, some parameters can be monitored, such as the duration of equal discharging voltage difference, the interval of equal charging voltage difference at different experiment cycles, also exhibit a degradation trend. In order to make more accurate SOH and remaining useful life (RUL) estimations of an on-line operating battery, in this paper, the relevance vector machine (RVM) is applied to quantify the relationship between those monitoring parameters and capacity data. Based on the deduced model, the capacity could be extrapolated with the corresponding monitoring parameters. Moreover, feature vector selection (FVS) is used to remove redundant points in the input data. It improves the sparsity of relevance vectors (RVs) and decreases the memory-consuming. In the end, Battery degradation datasets from NASA demonstrated the approach has good RUL prediction accuracy, higher sparsity compared to RVM.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132895232","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: 23

Prognostics of Lithium ion battery using functional principal component analysis 用功能主成分分析预测锂离子电池性能

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998299

Jian Guo, Z. Li

引用次数: 9

Diagnosis for systems with multi-component wear interactions 多组分磨损相互作用系统的诊断

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998312

Roy Assaf, P. Do, P. Scarf, S. Nefti-Meziani

引用次数: 6

Long Short-Term Memory Network for Remaining Useful Life estimation 用于剩余使用寿命估计的长短期记忆网络

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998311

Shuai Zheng, Kosta Ristovski, Ahmed K. Farahat, Chetan Gupta

{"title":"Long Short-Term Memory Network for Remaining Useful Life estimation","authors":"Shuai Zheng, Kosta Ristovski, Ahmed K. Farahat, Chetan Gupta","doi":"10.1109/ICPHM.2017.7998311","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998311","url":null,"abstract":"Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a critical role in Prognostics and Health Management(PHM). Data driven approaches for RUL estimation use sensor data and operational data to estimate RUL. Traditional regression based approaches and recent Convolutional Neural Network (CNN) approach use features created from sliding windows to build models. However, sequence information is not fully considered in these approaches. Sequence learning models such as Hidden Markov Models (HMMs) and Recurrent Neural Networks (RNNs) have flaws when modeling sequence information. HMMs are limited to discrete hidden states and are known to have issues when modeling long-term dependencies in the data. RNNs also have issues with long-term dependencies. In this work, we propose a Long Short-Term Memory (LSTM) approach for RUL estimation, which can make full use of the sensor sequence information and expose hidden patterns within sensor data with multiple operating conditions, fault and degradation models. Extensive experiments using three widely adopted Prognostics and Health Management data sets show that LSTM for RUL estimation significantly outperforms traditional approaches for RUL estimation as well as Convolutional Neural Network (CNN).","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866670","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: 475

Real-time remote monitoring of an air compressor using MTConnect standard protocol 使用MTConnect标准协议对空压机进行实时远程监控

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998314

N. Verma, Raghav Dev, N. K. Dhar, D. Singh, A. Salour

引用次数: 4

Using independent component analysis scheme for helicopter main gearbox bearing defect identification 采用独立分量分析方案进行直升机主齿轮箱轴承缺陷识别

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998337

F. Duan, Michael Corsar, Linghao Zhou, D. Mba

引用次数: 6

Health-aware hierarchical control for smart manufacturing using reinforcement learning 基于强化学习的智能制造健康感知分层控制

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998303

Benjamin Y. Choo, Stephen C. Adams, P. Beling

{"title":"Health-aware hierarchical control for smart manufacturing using reinforcement learning","authors":"Benjamin Y. Choo, Stephen C. Adams, P. Beling","doi":"10.1109/ICPHM.2017.7998303","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998303","url":null,"abstract":"Manufacturing facilities are laid out in a natural hierarchy of assembly lines, work cells, machines, and components. Currently, prognostics and health management (PHM) information is confined to the lowest levels of this hierarchy and finds primary use in decisions and control policies for machine maintenance and replacement. For the smart manufacturing systems of the future, however, PHM information should be passed to all levels of the hierarchy and incorporated into high level decision making about production quantities, rates, and locations. This paper proposes a hierarchical control methodology that passes PHM health estimates up the hierarchy and optimization objectives down the hierarchy. Individual nodes in the hierarchy are modeled as Markov decision processes (MDPs) and reinforcement learning is used to estimate optimal policies. This work makes several contributions to the PHM community. First, we propose a novel model of a control system that makes uses of health information throughout the manufacturing hierarchy. Second, we define a reinforcement learning based approach to solving the MDPs for optimal or near-optimal policies. Third, we illustrate the method on a numerical example based on a simulation of a real-world manufacturing environment.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133486744","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

A feature extraction method based on probabilistic Principal components analysis and sampling importance resampling for bearing fault detection 基于概率主成分分析和采样重要性重采样的轴承故障特征提取方法

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998301

Yixiang Huang, Yanming Li, Chengliang Liu, Xiao Liu

{"title":"A feature extraction method based on probabilistic Principal components analysis and sampling importance resampling for bearing fault detection","authors":"Yixiang Huang, Yanming Li, Chengliang Liu, Xiao Liu","doi":"10.1109/ICPHM.2017.7998301","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998301","url":null,"abstract":"The applications of monitoring the equipment online are often limited by the practical signal processing, limited by storage and transferring capacities. The efficiency is a key problem. Thus, a novel highly efficient feature extraction model for evaluating the equipment performance is proposed, which consists of the probabilistic Principal components analysis and the second generation wavelet analysis with the sampling importance resampling method. It starts by transforming raw signals into the wavelet domain by the second generation wavelet packet analysis. Then a sampling-importance resampling procedure is applied to reduce the redundancy and retain the distribution information. The obtained features are then fed into a probabilistic principal components analysis model to reduce the dimensionality. the proposed model is validated in a rolling element bearing test which shows that it is not only effective in diagnosis, but also may save the processing time of feature extraction, the data transfer bandwidth and the storage space.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128205758","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

A method for the maintainability assessment at design stage based on maintainability attributes 一种基于可维护性属性的设计阶段可维护性评估方法

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998326

Xu Luo, Z. Ge, F. Guan, Yongmin Yang

引用次数: 6

Advanced packaging for wireless sensor nodes in cyber-physical systems - impacts of multifunctionality and miniaturization on the environment 网络物理系统中无线传感器节点的先进封装。多功能性和小型化对环境的影响

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-01 DOI: 10.1109/ICPHM.2017.7998324

E. Wagner, Christian Bohme, S. Benecke, Nils N. Nissen, K. Lang

{"title":"Advanced packaging for wireless sensor nodes in cyber-physical systems - impacts of multifunctionality and miniaturization on the environment","authors":"E. Wagner, Christian Bohme, S. Benecke, Nils N. Nissen, K. Lang","doi":"10.1109/ICPHM.2017.7998324","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998324","url":null,"abstract":"Advances in microsystem technology have led to an increasing trend of cyber physical system integration into industrial environments for prognostic health monitoring. Harsh environmental conditions require wireless sensor nodes (WSNs) that are robustness and reliable whilst meeting the requirements of miniaturization, functional integrity and energy efficiency at the same time. Moreover design and application of WSNs need to meet the target of an optimized trade-off between direct impacts associated with the life cycle of the electronics as well as indirect impacts triggered through the usage of electronics. In this contribution, functionally equivalent sensor designs are discussed that represent three evolutionary stages of electronics packaging. The most advanced sensor generation presented applies size-optimized routing, embedded active and passive devices and bare-die assembly. The results of a life-cycle based environmental assessment conducted for the three concepts of WSNs are presented. The study allows for the quantification and comparison of environmental impacts associated with the process of miniaturizing cyber physical system hardware by means of advanced packaging.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123056203","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