2019 Prognostics and System Health Management Conference (PHM-Paris)最新文献

Value of Information from Condition Inspection for a Gamma Degradation Process 伽玛退化过程状态检测信息的价值

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-03 DOI: 10.1109/PHM-Paris.2019.00040

W. Fauriat, E. Zio

引用次数: 0

Health Indicator Construction for System Health Assessment in Smart Manufacturing 面向智能制造系统健康评估的健康指标构建

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00016

M. Soualhi, K. Nguyen, K. Medjaher, D. Lebel, D. Cazaban

引用次数: 7

Data-Driven Anomaly Detection for UAV Sensor Data Based on Deep Learning Prediction Model 基于深度学习预测模型的无人机传感器数据驱动异常检测

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00055

Benkuan Wang, Zeyang Wang, Liansheng Liu, Datong Liu, Xiyuan Peng

{"title":"Data-Driven Anomaly Detection for UAV Sensor Data Based on Deep Learning Prediction Model","authors":"Benkuan Wang, Zeyang Wang, Liansheng Liu, Datong Liu, Xiyuan Peng","doi":"10.1109/PHM-Paris.2019.00055","DOIUrl":"https://doi.org/10.1109/PHM-Paris.2019.00055","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) can accomplish various specific tasks and play an increasingly essential role in military, industrial and civil fields. However, the safety of the UAV is lower than that of manned aircraft, and great economic loss is caused due to its relatively high failure rate. Therefore, it is of great significance to study the anomaly detection method for the UAV system. In recent years, the deep learning method has been widely applied in various fields due to its outstanding advantages such as strong ability to approximate complex functions and automatic feature extraction. In this paper, a Long Short Term Memory (LSTM) Recurrent Neural Network method is proposed for the UAV anomaly detection. Firstly, a prediction model is formulated based on the training data set which contains only normal data, then the data at next time can be predicted. Secondly, according to the prediction results in train phase, we give an estimation of the prediction uncertainty. Finally, anomaly detection is achieved by comparing the prediction value with the uncertain interval. Real UAV sensor data with point anomalies in north velocity and pneumatic lifting velocity are used to verify the proposed method, and experimental results show that the proposed method can effectively detect point anomalies.","PeriodicalId":119499,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Paris)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633190","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: 34

Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals 基于声发射、振动和电流信号的深度学习齿轮点蚀严重程度评估

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00042

Rubén Medina, M. Cerrada, Diego Cabrera, Réne-Vinicio Sánchez, Chuan Li, José Valente de Oliveira

引用次数: 7

Design Scheme of Fault Diagnosis System for Rollers of Coal Mills 煤机轧辊故障诊断系统的设计方案

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00028

Yu-wei Du

引用次数: 1

A Prognosis Methodology Based on Enhanced Lolimot Algorithm Using Historical Data 基于增强型Lolimot算法的历史数据预测方法

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00014

S. Razavi, T. A. Najafabadi, A. Mahmoodian

引用次数: 3

A Domain Adaptive Convolutional LSTM Model for Prognostic Remaining Useful Life Estimation Under Variant Conditions 一种域自适应卷积LSTM模型在变条件下的预测剩余使用寿命估计

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00030

Shuyang Yu, Zhenyu Wu, Xinning Zhu, M. Pecht

{"title":"A Domain Adaptive Convolutional LSTM Model for Prognostic Remaining Useful Life Estimation Under Variant Conditions","authors":"Shuyang Yu, Zhenyu Wu, Xinning Zhu, M. Pecht","doi":"10.1109/PHM-Paris.2019.00030","DOIUrl":"https://doi.org/10.1109/PHM-Paris.2019.00030","url":null,"abstract":"In the age of industry 4.0 and smart manufacturing, a large volume of sensor data is produced from cyber-physical systems (CPS) and prediction of remaining useful life (RUL) of a machine or system becomes crucial for prognostics and health management (PHM). Several linear regression and deep learning models have been studied to extract features from segmented time windows and learn the degradation patterns. However, distributions of features are varying between source learning domain and target test domains, due to different working conditions and environments. Thus, the generalization of traditional methods will be influenced, which leads to performance degradation. This paper develops a domain adaptive CNN-LSTM (DACL) model to predict the RUL of a system based on the multi-dimensional sensor data. The DACL model combines the CNN and LSTM with domain adaptive transfer mechanism and takes the operating conditions into consideration. The features extracted by CNN of both source and target data are transformed to a higher dimensional space by reproducing kernel Hilbert space (RKHS) and the loss function is compensated by using maximum mean discrepancy (MMD) to reduce the distributions discrepancy. The model is evaluated on C-MAPSS dataset and demonstrate its performance improvement by comparing with previous methods.","PeriodicalId":119499,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Paris)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121310945","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

Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM 基于WP重构能量熵和PSO-LSSVM的滚动轴承故障诊断

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00011

Hong-Mei Yan, H. Mu, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen

引用次数: 0

The Relationship between Individualism, Collectivism and Conflict Handling Styles of Healthcare Employees 医疗保健员工个人主义、集体主义与冲突处理方式的关系

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00036

Seb Aslan, Şerife Güzel, Demet Akarçay Ulutaş

引用次数: 2

Fault Diagnosis of Planetary Gearbox Based on Signal Denoising and Convolutional Neural Network 基于信号去噪和卷积神经网络的行星齿轮箱故障诊断

2019 Prognostics and System Health Management Conference (PHM-Paris) Pub Date : 2019-05-02 DOI: 10.1109/PHM-Paris.2019.00024

Guo-dong Sun, You-ren Wang, Can-fei Sun

引用次数: 6