A Combined Model-Based and Data-Driven Prognostic Approach for Aircraft System Life Management

2011 ◽  
pp. 363-394 ◽  
Author(s):  
Marcos Orchard ◽  
George Vachtsevanos ◽  
Kai Goebel
Keyword(s):  
Data Driven ◽  
Combined Model ◽  
Life Management ◽  
Model Based ◽  
Aircraft System ◽  
System Life

scholarly journals Framework for Unifying Model-based and Data-driven Fault Diagnosis

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamed Khorasgani ◽  
Ahmed Farahat ◽  
Kosta Ristovski ◽  
Chetan Gupta ◽  
Gautam Biswas
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Method ◽  
Fault Detection And Isolation ◽  
The Other ◽  
Data Driven ◽  
Isolate System ◽  
Combined Model ◽  
Model Based ◽  
Abnormal Behaviors

Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several researchers have combined model-based diagnosis techniques with datadriven approaches to propose hybrid1solutions for fault diagnosis. Many researchers have proposed methods to integrate specific approaches. In this paper, we demonstrate that data-driven and model-based diagnosis methods follow a similar procedure and can be represented by a general unifying framework. This unifying framework for fault detection and isolation can be used to integrate different methodologies developed by two communities. As a case study, we use the proposed framework to build a crossover solution for fault diagnosis in a wind turbine benchmark. In this case study, we show that it is possible to achieve a better diagnosis performance by using a hybrid method that follows the proposed framework.

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.

scholarly journals Multi-Inputs and Multi-Outputs equivalent model based on data driven controller for a robotic system

2020 ◽  
Vol 53 (2) ◽  
pp. 9784-9789
Author(s):  
Josué Gómez ◽  
Chidentree Treesatayapun ◽  
América Morales
Keyword(s):  
Robotic System ◽  
Data Driven ◽  
Equivalent Model ◽  
Model Based

Data-driven Model-based Detection of Malicious Insiders via Physical Access Logs

2019 ◽  
Vol 29 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Carmen Cheh ◽  
Uttam Thakore ◽  
Ahmed Fawaz ◽  
Binbin Chen ◽  
William G. Temple ◽  
...  
Keyword(s):  
Data Driven ◽  
Model Based ◽  
Malicious Insiders ◽  
Access Logs ◽  
Physical Access

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals A DATA-DRIVEN APPROACH TO USER-EXPERIENCE-FOCUSED MODEL-BASED ROADMAPPING FOR NEW PRODUCT PLANNING

2021 ◽  
Vol 1 ◽  
pp. 61-70
Author(s):  
Ilia Iuskevich ◽  
Andreas-Makoto Hein ◽  
Kahina Amokrane-Ferka ◽  
Abdelkrim Doufene ◽  
Marija Jankovic
Keyword(s):  
New Product Development ◽  
User Experience ◽  
Planning Process ◽  
New Product ◽  
Data Driven ◽  
User Needs ◽  
User Testing ◽  
Model Based ◽  
Data Driven Approach ◽  
To Receive

AbstractUser experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

Direct determination of the concentration dependence of diffusivities using combined model-based Raman and NMR experiments

2009 ◽  
Vol 277 (2) ◽  
pp. 96-106 ◽  
Author(s):  
E. Kriesten ◽  
M.A. Voda ◽  
A. Bardow ◽  
V. Göke ◽  
F. Casanova ◽  
...  
Keyword(s):  
Concentration Dependence ◽  
Direct Determination ◽  
Combined Model ◽  
Model Based
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