2019 Prognostics and System Health Management Conference (PHM-Qingdao)最新文献_第4页

Data Zeroing Based on Correlation and Linear Interpolation of the Blade Tip-Timing Data 基于叶尖计时数据相关性和线性插值的数据调零

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942861

Y. Yu, Yunqiang Wu, Lin Yue

引用次数: 0

Multi-Task Learning and Attention Mechanism Based Long Short-Term Memory for Temperature Prediction of EMU Bearing 基于多任务学习和注意机制的动车组轴承温度预测

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942914

Yaohua Chen, C. Zhang, Ning Zhang, Yiting Chen, Huan Wang

{"title":"Multi-Task Learning and Attention Mechanism Based Long Short-Term Memory for Temperature Prediction of EMU Bearing","authors":"Yaohua Chen, C. Zhang, Ning Zhang, Yiting Chen, Huan Wang","doi":"10.1109/phm-qingdao46334.2019.8942914","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942914","url":null,"abstract":"The traction motor is one of the key components that plays an important role in ensuring the safety and stability of the running EMU (Electric Multiple Units). The running state of the traction motor can be determined through monitoring and predicting the change of EMU bearing temperature. In this paper, we propose a Long Short-Term Memory Neural Network based on Multi-task Learning and Attention Mechanism for the bearing temperature prediction in view of the complex influencing factors of bearing temperature in train operation. The model learns the characteristics of temperature sensors in different positions jointly through multi-task learning. And the Long Short-Term Memory Neural Network based on Attention Mechanism is used to consider the influence of current operating conditions and previous train records on bearing temperature in different degrees. So the model takes various influencing factors and spatial-temporal correlation into consideration. The experimental results with actual EMU datasets show that our method outperforms the baseline approaches.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763311","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: 5

Development of Vibration-Based Health Indexes for Bearing Remaining Useful Life Prediction 基于振动健康指标的轴承剩余使用寿命预测

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8943002

Xiaohang Jin, Z. Que, Yi Sun

引用次数: 0

A Health Indicator Construction Method based on Deep Belief Network for Remaining Useful Life Prediction 基于深度信念网络的剩余使用寿命预测健康指标构建方法

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8943014

Ruihua Jiao, Kai-xiang Peng, Jie Dong, Kai Zhang, Chuang-jian Zhang

引用次数: 2

Feasibility Study of Online Monitoring Using the Fiber Bragg Grating Sensor for Geared System 光纤光栅传感器在线监测齿轮传动系统的可行性研究

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8943060

Shenghao Shi, Yongzhi Qu, Jinglin Wang, Liu Hong, J. Dhupia, Zude Zhou

{"title":"Feasibility Study of Online Monitoring Using the Fiber Bragg Grating Sensor for Geared System","authors":"Shenghao Shi, Yongzhi Qu, Jinglin Wang, Liu Hong, J. Dhupia, Zude Zhou","doi":"10.1109/phm-qingdao46334.2019.8943060","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943060","url":null,"abstract":"The gearbox is one of the most common and important components in the drivetrains. Thus, the online monitoring of the dynamic behavior of geared system is crucial for the optimization, diagnosis and prognosis of the drivetrains. The conventional online monitoring system for gearboxes is to use the vibration sensor mounted on the gear housing. However, in the measured housing vibration signal, the dynamic response of the monitored geared pair is usually distorted, which is caused by the complex transfer path of the vibration. Therefore, to advance the art of online monitoring of gearboxes, this work proposes to employ the fiber Bragg grating as the strain sensor to mount near the gear mesh region. The experimental assessment of the feasibility of the fiber Bragg grating based online monitoring system is conducted in a laboratory fixed-axis spur gearbox. To validate and analyze the measurement from the fiber Bragg grating system, a gear mesh model is developed using the finite element method. The comparison between the measurement and theoretical simulation show the proposed fiber Bragg grating based online monitoring system is capable to capture the variation of the root strain during the gear mesh process. This result proves the proposed technique has a promising potential in developing a commercial online monitoring system to measure the subtle dynamic behavior of gearboxes.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130078687","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

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8943006

Xiaochuan Li, D. Mba, Tianran Lin

引用次数: 3

Bearing Diagnosis Accuracy Comparison Using Convolutional Neural Network with Time/Frequency Domain Signals 基于时频域信号的卷积神经网络轴承诊断精度比较

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942879

D. He, W. Guo, Mao He

{"title":"Bearing Diagnosis Accuracy Comparison Using Convolutional Neural Network with Time/Frequency Domain Signals","authors":"D. He, W. Guo, Mao He","doi":"10.1109/phm-qingdao46334.2019.8942879","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942879","url":null,"abstract":"Deep learning is the most attractive topic in the field of machine learning and relevant applications. Owing to the strong learning ability of the convolutional neural network (CNN), it integrates the feature extraction from raw data and classification as a complete learning process and makes the bearing fault diagnosis intelligent. In the published results, the inputs of the CNN may be the raw temporal waveform of vibration, its processed waveform or converted 2D images. In this paper, focusing on the diagnosis accuracy of rolling bearings, a comparative study is conducted among the inputs using the raw temporal waveform, the frequency spectrum, and the envelope spectrum of a vibration signal. First, an appropriate classification model based on the CNN is constructed. Then, experimental data from bearing with real damages are collected and then transformed and converted into some small gray pixel images for training and testing the CNN model. Finally, the classification accuracies using three signals are compared. The results indicate that the diagnosis performances using the above three signals are close when the trained CNN models are stable; among them the model using the frequency spectrum of the vibration signal is a little better than the models using the other two signals, which may be a reference for further investigating the deep learning used in the field of bearing diagnosis.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121965145","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 Fault Diagnosis Method for Speed Sensor of High-Speed Train 高速列车速度传感器故障诊断方法研究

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942951

Mengling Wu, Gang Liu, Jinjun Lu, Xiaofeng Geng

{"title":"Research on Fault Diagnosis Method for Speed Sensor of High-Speed Train","authors":"Mengling Wu, Gang Liu, Jinjun Lu, Xiaofeng Geng","doi":"10.1109/phm-qingdao46334.2019.8942951","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942951","url":null,"abstract":"Speed sensors installed on the axes of high-speed train will lead to faults due to the vibration and electromagnetic interference during train operation. At present the braking system can't detect all faults of speed sensor but misdirect the axle lock fault, which affects the safety of train operation. Therefore, this paper proposes an integral intelligent fault diagnosis method for speed sensor of high-speed train brake system, which realizes real-time detection of speed sensor anomalies and accurate location of the axis of the speed sensor fault. Firstly, the traditional principal component analysis method is improved by proposing a comprehensive monitoring statistic to realize real-time fault detection of speed sensor. Then, the modified reconstruction based contribution plot based on the idea of combination maximization is adopted to achieve accurate fault location of speed sensor. In addition, the fault injection experiments are conducted, the results prove the method can diagnose the fault of speed sensor accurately and effectively, and solve the hidden trouble of high-speed train operation.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121223354","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

Application and Design of PHM in Aircraft’s Integrated Modular Mission System PHM在飞机集成模块化任务系统中的应用与设计

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942896

Wen Jia, Luo Haimin, W. Xiao

引用次数: 1

A Quick-response Failure Detection Model of GNSS Airborne System GNSS机载系统快速响应故障检测模型

2019 Prognostics and System Health Management Conference (PHM-Qingdao) Pub Date : 2019-10-01 DOI: 10.1109/phm-qingdao46334.2019.8942872

M. Zan, Wang Peng, L. Ruihua, Huang Jianbo

引用次数: 0