2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)最新文献_第6页

A Review of Fault Diagnosis Methods Based on Machine Learning Patterns 基于机器学习模式的故障诊断方法综述

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612779

Zhu Xiao, Zhe Cheng, Yuehao Li

引用次数: 7

The Abnormal Detection Strategy for Spacecraft Components with Multi-dimension Parameters 多维参数航天器部件异常检测策略研究

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612759

Shouwen Liu, Taichun Qin, Shouqing Huang, Yunfei Jia, Guangyuan Zheng, Wanning Yao, Baohui Wang

引用次数: 0

Integrated System Health Management Research and Application on Satellite 卫星综合系统健康管理研究与应用

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612928

Yufei Xu, Xianglong Kong, Wei Ma, Zhu Zhu, Jianqiao Zhang

{"title":"Integrated System Health Management Research and Application on Satellite","authors":"Yufei Xu, Xianglong Kong, Wei Ma, Zhu Zhu, Jianqiao Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9612928","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612928","url":null,"abstract":"Integrated system health management (ISHM) technology is the extension of fault diagnosis and fault tolerant control and also is an important research direction of satellite engineering in the future. The ISHM of satellite, which can support satellite self-sufficient, aims to improve the reliability, safety and economy. In this paper, the overall architecture of the ISHM of on-orbit satellite is firstly constructed, which is divided into several layers, including system-level layer, subsystem layer, reasoning and diagnosis layer and single machine layer. The functions and tasks of each layer are analyzed, and the differences between ISHM and traditional fault diagnosis methods are proposed. Secondly, according to the analysis of history fault data, the subsystems who are prone to fault are studied and the ISHM schemes for these subsystems are designed. Finally, key technologies for achieving ISHM of satellite, such as the use of advanced distributed sensor system, improvement of software and hardware on board, and the application of intelligent algorithms, are illustrated and discussed.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115827045","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

RUSHAP: A Unified approach to interpret Deep Learning model for Remaining Useful Life Estimation RUSHAP:一种统一的方法来解释深度学习模型的剩余使用寿命估计

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612945

Yi-Lin Wang, Yuanxiang Li, Yuxuan Zhang, Yongsheng Yang, Lei Liu

{"title":"RUSHAP: A Unified approach to interpret Deep Learning model for Remaining Useful Life Estimation","authors":"Yi-Lin Wang, Yuanxiang Li, Yuxuan Zhang, Yongsheng Yang, Lei Liu","doi":"10.1109/PHM-Nanjing52125.2021.9612945","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612945","url":null,"abstract":"The maintenance decision models based on Prognostic and Health Management (PHM) technology have significantly improved complex equipment submission reliability and economy. One of the essential techniques of PHM is predicting the remaining useful life (RUL) of the system or the system components. Compared to other RUL prediction methods, deep learning has become a research hotspot due to its automatic feature extraction capability, big data process efficiency, powerful representation of complex mappings, and “end-to-end” learning process. However, deep learning (DL) models are with high complexity, huge parameter quantity, and low interpretability, namely black box models. Lack of interpretability limits their application and development in “high-risk” fields such as aviation maintenance decision-making. To solve this problem, we propose a universal RUL interpretation method for DL named as RUL Shapley Additive explanation (RUSHAP). RUSHAP uses the input and output of the DL model to calculate the Shapley value and then obtain the interpretation from three different hierarchies, i.e., time level, feature level, and component level. With RUSHAP, it is possible to go from only knowing the RUL of the system to locating fault state points, observing the declining trend of sensor data, and evaluating the health status of subsystem, achieving partial white-boxing of the RUL prediction DL model. RUSHAP can also compare the advantages and disadvantages between different DL models, giving references for model debugging and ideas for model design.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124541575","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

Research on single image super resolution enhancement algorithm based on deep learning 基于深度学习的单幅图像超分辨率增强算法研究

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612927

Ming Han, Han Liu

引用次数: 0

A Novel Fault Detection Method Based on Multiple Features for Analog Circuits 一种基于多特征的模拟电路故障检测方法

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612952

Tianyu Gao, Jingli Yang, Jianfeng Wang, Shouda Jiang

{"title":"A Novel Fault Detection Method Based on Multiple Features for Analog Circuits","authors":"Tianyu Gao, Jingli Yang, Jianfeng Wang, Shouda Jiang","doi":"10.1109/PHM-Nanjing52125.2021.9612952","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612952","url":null,"abstract":"The reliability and security of analog circuits are becoming increasingly significant. Fault diagnosis methods can identify fault classes of analog circuits, thus locating the fault components. However, the fault diagnosis methods based on multi-classification learning framework suffer from the problem of desirable classification effect in the case of lack of fault samples. To address these issues, a fault detection method based on multiple features for analog circuits is proposed in this paper. By learning only normal samples to obtain control limits, the proposed fault detection method can effectively determine the health states of analog circuits. First, features of the output signals of the circuit under test (CUT) in the time domain, frequency domain, and time-frequency domain are calculated to comprehensively reflect its states. In addition, the construction method of related similarity (RS) features is introduced to achieve feature enhancement, which further explores the essential information in the features. Then, to remove redundant features, the feature selection is adaptively performed by using the quantum particle swarm optimization (QPSO) algorithm, where the fitness is the improved Wilks statistic (IWS). Finally, the feature vectors are transmitted to the fault detection model based on kernel principal component analysis (KPCA) to identify the health states of CUT. The experimental results indicate that the proposed method exhibits excellent detection performance for analog circuits in the case of lack of fault samples.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114844367","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 Post-Warranty Maintenance Strategy Considering Product Age 考虑产品使用年限的保修期后维修策略

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9613115

Peng Liu, G. Wang, Yi-xiong Zhang

引用次数: 0

Research on Health Assessment System of Aircraft Power Supply System 飞机供电系统健康评估系统研究

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612944

Yang Dong, Xiao DingYang, Jiayi Liang, Qin Zhou Lin

{"title":"Research on Health Assessment System of Aircraft Power Supply System","authors":"Yang Dong, Xiao DingYang, Jiayi Liang, Qin Zhou Lin","doi":"10.1109/PHM-Nanjing52125.2021.9612944","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612944","url":null,"abstract":"The role of aircraft power supply system on flight safety is irreplaceable, and the lack of system-level assessment makes it necessary to assess its health status. The paper puts forward the concept of failure mode degradation state value, unifies the research route of equipment health assessment based on failure and degradation, proposes a calculation method for the importance of model nodes, and establishes a hierarchical model of aircraft power supply system health assessment. According to the comprehensive evaluation characteristics of the multi-attribute indicators that are coupled to the status of each component of the power supply system, the cloud model, entropy weight method and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) are used to comprehensively calculate the risk ranking of failure modes. And the proportion, the influence weight of each layer model node on the upper layer node is obtained, and the comprehensive risk evaluation is completed. Then, according to the degradation degree of the failure mode degradation state value, the entropy weight method is used to improve the weight of the failure mode degradation state value, and the effectiveness of the evaluation algorithm is evaluated and improved. Finally, the health status of each subsystem and the health of the equipment are displayed with a radar chart, evaluation result. It provides an effective implementation path for the health assessment of the aircraft power supply system.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123571259","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 two-stage approach for modeling inverse S-shaped wear processes of cutting tools 刀具反s形磨损过程的两阶段建模方法

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9613013

R. Jiang

{"title":"A two-stage approach for modeling inverse S-shaped wear processes of cutting tools","authors":"R. Jiang","doi":"10.1109/PHM-Nanjing52125.2021.9613013","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613013","url":null,"abstract":"Wear is one of major failure causes of cutting tools. Monitoring the wear process of a cutting tool and predicting its residual life have attracted wide attentions. A stochastic wear process model that relates the wear amount to cumulative cutting time is needed so as to make the inspection and replacement decisions of the cutting tool. The wear amount as a function of cutting time is often inverse S-shaped. That is, the wear rate curve is bathtub-shaped. The works that explicitly model inverse S-shaped wear processes are rare. This paper presents a two-stage approach for modeling this type of wear processes. The proposed approach divides the process into two stages with the inflection point of the wear curve as the boundary of stages. The task in the first stage is to collect data and the tasks in the second stage are to predict residual life and make inspection and replacement decisions. The stochastic wear process model obtained from the proposed approach is simple and realistic, and does not need many data. A real-world example is included to illustrate the simplicity and appropriateness of the proposed approach.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123654923","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

An Information Transmission Model For Offshore Wind Turbines Based On Multi-variable Coupling Relationship 基于多变量耦合关系的海上风电机组信息传递模型

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612777

Jing Huang, Rongxi Wang, Zhiyong Gao, Jianmin Gao, W. Deng, Zhen Wang

引用次数: 0