scholarly journals Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

2021 ◽  
Vol 9 (4) ◽  
pp. 897-909
Author(s):  
Yanbo Chen ◽  
Hao Chen ◽  
Yang Jiao ◽  
Jin Ma ◽  
Yuzhang Lin
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
On Line

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

A Framework for Big Data Driven On-Line Monitoring of Tool Wear

2021 ◽  
Author(s):  
Yong Gui ◽  
Sheng Leng ◽  
Zhiqiang Dai ◽  
Jiyuan Wu
Keyword(s):  
Big Data ◽  
Tool Wear ◽  
Data Driven ◽  
On Line

scholarly journals BR-Sensor: An On-line Data-driven Soft Sensor of Downhole Pressure∗∗Final support from Petrobas S.A. is acknowledged.

2015 ◽  
Vol 48 (6) ◽  
pp. 311-316 ◽  
Author(s):  
Edson F.A. Rezende ◽  
Alex F. Teixeira ◽  
Eduardo M.A.M. Mendes
Keyword(s):  
Soft Sensor ◽  
Data Driven ◽  
On Line
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