2016 Prognostics and System Health Management Conference (PHM-Chengdu)最新文献_第2页

A prognostic model for degrading systems with randomly arriving shocks 具有随机冲击的退化系统的预测模型

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819779

Xiaosheng Si, Tianmei Li, Qi Zhang, Zhengxin Zhang

引用次数: 3

Analysis and development of the integrated system of operation maintenance support for helicopter 直升机作战维护保障综合系统的分析与开发

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819868

J. Ding, Shengbin Chen, M. Zeng

引用次数: 0

Research advances in fault diagnosis and prognostic based on deep learning 基于深度学习的故障诊断与预测研究进展

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819786

Guangquan Zhao, Guohui Zhang, Qiangqiang Ge, Xiaoyong Liu

{"title":"Research advances in fault diagnosis and prognostic based on deep learning","authors":"Guangquan Zhao, Guohui Zhang, Qiangqiang Ge, Xiaoyong Liu","doi":"10.1109/PHM.2016.7819786","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819786","url":null,"abstract":"Aiming to condition based maintenance for complex equipment, numerous intelligent fault diagnosis and prognostic methods based on machine learning have been researched. Compared with the traditional shallow models, which have problems of lacking expression capacity and existing the curse of dimensionality, using deep learning theory can effectively mine characteristics and accurately recognize the health condition. In consequence, fault diagnosis and prognostic based on deep learning have turned into an innovative and promising research field. This paper gives a review of fault diagnosis and prognostic based on deep learning. First of all, a brief introduction to deep learning architecture and the framework of fault diagnosis based on deep learning is described. Second, tracking describes the latest progress of fault diagnosis and prognostic based on deep learning in chronological order. In this section, the deep learning methods used in fault diagnosis and prognostic are discussed, including Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN). Then the engineering application fields are summarized, such as mechanical equipment diagnosis, electrical equipment diagnosis, etc. Finally, this paper indicates the potential future research issues in this field.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128619727","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: 79

A method of health monitoring on helicopter speed reducer based on FMECA 基于FMECA的直升机减速器健康监测方法

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819768

Liqing Xu, Cheng Zhang, Huiyun Wang, Xingwang Li, Yong Shen, Zhaohua He

引用次数: 4

Development of a prognostics and health management system for the railway infrastructure — Review and methodology 铁路基础设施预测和健康管理系统的发展。审查和方法

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819783

M. Brahimi, K. Medjaher, M. Leouatni, N. Zerhouni

{"title":"Development of a prognostics and health management system for the railway infrastructure — Review and methodology","authors":"M. Brahimi, K. Medjaher, M. Leouatni, N. Zerhouni","doi":"10.1109/PHM.2016.7819783","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819783","url":null,"abstract":"The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123978083","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

Analysis of tram conflict risk with pedestrian at the intersection based on ATA 基于ATA的交叉口有轨电车与行人冲突风险分析

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819890

Dongxiu Ou, Huang Yan, Yang Yang, Jing Xu

引用次数: 1

A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws 基于深度学习的滚珠丝杠退化监测多传感器数据融合方法

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819792

Li Zhang, Hongli Gao

{"title":"A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws","authors":"Li Zhang, Hongli Gao","doi":"10.1109/PHM.2016.7819792","DOIUrl":"https://doi.org/10.1109/PHM.2016.7819792","url":null,"abstract":"As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.","PeriodicalId":202597,"journal":{"name":"2016 Prognostics and System Health Management Conference (PHM-Chengdu)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732015","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

Data-based anomaly detection model for solar array power of in-orbit satellites 基于数据的在轨卫星太阳阵功率异常检测模型

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819878

Guoyong Zhang, Jun Zhou, Fengning Han, Datong Liu

引用次数: 3

A testability modeling method for analog circuit fault prediction 模拟电路故障预测的可测试性建模方法

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819944

Wenkui Hou, Xiaolin Fan

引用次数: 1

Optimization of maintenance strategy for multi-component system subject to degradation process 退化过程下多部件系统维修策略的优化

2016 Prognostics and System Health Management Conference (PHM-Chengdu) Pub Date : 2016-10-01 DOI: 10.1109/PHM.2016.7819854

Shuyang Guo, Yufeng Sun, Guangyan Zhao, Zhiwei Chen

引用次数: 6