2019 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第6页

A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings 滚动轴承故障预测的深度学习方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819442

Mohammadkazem Sadoughi, Hao Lu, Chao Hu

{"title":"A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings","authors":"Mohammadkazem Sadoughi, Hao Lu, Chao Hu","doi":"10.1109/ICPHM.2019.8819442","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819442","url":null,"abstract":"Remaining useful life (RUL) prediction of rotating machine components is essential to enabling predictive maintenance of industrial and agricultural machinery. This paper presents a novel deep learning approach for failure prognostics of rolling element bearings. The proposed approach has three unique features: (1) it employs a new data augmentation technique to improve the accuracy and robustness of RUL prediction in cases where a deep learning model only has access to a small amount of training data; (2) it incorporates a robust feature learning strategy that integrates a physics-based feature extraction process with a data-driven process; and (3) it implements a new similarity-based approach for effectively capturing the true degradation trend of each individual bearing unit. A practical case study involving run-to-failure experiments of rolling element bearings on the PRONOSTIA platform is provided to assess the performance of the proposed approach. Results from the case study show the proposed deep learning approach produced higher accuracy in RUL prediction than an existing machine learning approach.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"46 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":"121840210","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: 8

Remaining useful life Prediction of air spring 空气弹簧剩余使用寿命预测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819413

F. Ahmadzadeh, Jonas Biteus, O. Steinert

{"title":"Remaining useful life Prediction of air spring","authors":"F. Ahmadzadeh, Jonas Biteus, O. Steinert","doi":"10.1109/ICPHM.2019.8819413","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819413","url":null,"abstract":"The remaining useful life estimation is an important function of an efficient prognostics and health management (PHM) system and can be used preventively to replace the component with the aim of avoiding a breakdown. The prediction of remaining life time of the air spring as one of the critical component of truck is the main goal of this research. A specific statistical model called mean residual life of Gompertz (MRL-Gompertz) has been considered to predict the remaining life time of the air spring, given that it has survived until a specific time. A set of sensors has been used to collect input variables for model. The time difference between start of usage and failure dates has been considered as life time of the air spring which is output of the model. The accuracy of the model prediction based on confusion matrix is more than 94%. This solution can be a basis for preventive maintenance because it reduces down time, vehicle off road (VOR) and use the components until the maximum life time before renewals takes place. It means huge saving in term of reducing cost of unplanned maintenance and increasing benefit by optimizing preventive maintenance.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"63 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":"123469529","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

Remaining Useful Life Estimation by Empirical Mode Decomposition and Ensemble Deep Convolution Neural Networks 基于经验模态分解和集成深度卷积神经网络的剩余使用寿命估计

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819373

Qingfeng Yao, Tianji Yang, Zhi Liu, Zeyu Zheng

引用次数: 4

Part Name Normalization 部件名称

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819386

Anne Kao, Nobal B. Niraula, Daniel Whyatt

{"title":"Part Name Normalization","authors":"Anne Kao, Nobal B. Niraula, Daniel Whyatt","doi":"10.1109/ICPHM.2019.8819386","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819386","url":null,"abstract":"Parts information plays a key role in prognostics and health management. However, expressions of parts often have a wide range of variations, spawned by typos, ad hoc abbreviations, acronyms, and incomplete names. Normalization of such terms is crucial for many applications. Part names post a major challenge also because they tend to be in the form of multi-word terms. In this paper, we propose a novel normalization method UNAMER (Unification and Normalization Analysis, Misspelling Evaluation and Recognition). It is a general method for identifying term variants, including multi-word term variants, and normalizing them under a canonical name. UNAMER does not rely on a predefined set of canonical terms, which is often hard to obtain. Given a term, UNAMER first identifies candidate variants by exploiting contextual information. It then uses a supervised machine learning model, trained using easy-to-generate examples, that leverages both contextual and lexical features to predict actual variants from the candidates. UNAMER further extends its capability to normalize multi-word parts, such as part names like ‘lt pnl’, ‘letf pnl’ and ‘lft panal’ for ‘left panel’ using a specialized linguistically motivated term alignment approach. UNAMER has been deployed in practical applications to normalize part names in the aerospace domain. We will use examples from these real-life applications to demonstrate and illustrate results from UNAMER.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"34 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":"125789529","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 Novel Bayesian Update Method for Parameter Reconstruction of Remaining Useful Life Prognostics 一种新的贝叶斯更新方法用于剩余使用寿命预测参数重建

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819377

Pengfei Wen, Shaowei Chen, Shuai Zhao, Yong Li, Yan Wang, Zhi Dou

引用次数: 1

Faults Analysis of Double Water Inner Cooled Synchronous Machines 双水内冷同步电机故障分析

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819412

Andong Wang, Junqing Li, Yangshuo Ma, Fuchun Sun

引用次数: 1

An Aging-Aware SOC Estimation Method for Lithium-Ion Batteries using XGBoost Algorithm 基于XGBoost算法的锂离子电池SOC老化感知估计方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819416

Fu Jiang, J. Yang, Yijun Cheng, Xiaoyong Zhang, Yingze Yang, Kai Gao, Jun Peng, Zhiwu Huang

引用次数: 13

Construction and Application of Failure Prediction and Health Management System for Bearing of Running Gear of Rolling Stock 铁道车辆走行齿轮轴承故障预测与健康管理系统的构建与应用

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819427

Hongzhi Song, Li Li, Xingkuan Yang, Zhenzhong Fan

引用次数: 1

Generating Mathematical Model of Equipment and Its Applications in PHM 设备数学模型的生成及其在PHM中的应用

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819402

Pushe Zhao, M. Kurihara, T. Noda, Hiroki Kashiwa, Masaki Hiyama

引用次数: 0

Building the Tower of Babel for Big Data 为大数据建造巴别塔

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819390

Adnan Mian, Richard Ronson

引用次数: 0