Time-Variant Digital Twin Modeling through the Kalman-Generalized Sparse Identification of Nonlinear Dynamics

Wang, Jingyi; Moreira, Jesús; Cao, Yankai; Gopaluni, R. Bhushan

doi:10.23919/acc53348.2022.9867786

Cited by 12 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of which, the latter case is a nonlinear system whose underlying dynamics were identified after nearly 30 years by field experts. SINDy has also been used directly with input/output data (known as generalized SINDy or GSINDy) and the Kalman filter to construct timevariant digital twin models (Wang et al, 2022). The Kalman-GSINDy approach was subsequently used along with the proper orthogonal decomposition (POD) to find the lifting functions to find reduced-order Koopman linear models for incorporation into MPC.…”

Section: Sparse Identification Modelingmentioning

confidence: 99%

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

Ou,

Abdullah,

Wang

et al. 2024

Chemical Engineering Research and Design

View full text Add to dashboard Cite

Section: Sparse Identification Modelingmentioning

confidence: 99%

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

Ou,

Abdullah,

Wang

et al. 2024

Chemical Engineering Research and Design

View full text Add to dashboard Cite

“…It concludes that the combination of DT with an EKF observer can prevent the battery from having severe failure, optimize its maintenance schedules, and conduct health monitoring with a focus on fault identification and correction. In study [ 22 ], the sparse detection of nonlinear dynamics challenges is integrated with the EKF algorithm to automatically recognize the time-variant DT models for online system monitoring. The robustness of the algorithm is validated through a simulation model based on Lorenz process and an industrial diesel hydrotreating case.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Model of Electric Drives Empowered by EKF

Ebadpour

Jamshidi

Talla

et al. 2023

Sensors

View full text Add to dashboard Cite

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber–physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system’s overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

show abstract

“…Utilizing data-driven simulations and digital twin (DT) technology supports building predictive models that enable real-time simulations that help with preventing undesirable scenarios. As a result, DT affords us a clear image of the system from a physical and operational point of view [13][14][15][16][17].…”

mentioning

confidence: 99%

“…The result of a state-of-the-art review reveals the significant gap in the studies of implementing DT in the UAV/UAS domain. In addition, digital twin models have demonstrated their effectiveness in handling complex systems by utilizing simplified models [16]. The proposed architecture of a predictive digital twin model involves five dimensions: the physical entity, digital model, real-life measurements (input), connection, and digital twin outputs (predictions/simulated data) [14].…”

mentioning

confidence: 99%

Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation

Aghazadeh Ardebili,

Ficarella,

Longo

et al. 2023

Aerospace

View full text Add to dashboard Cite

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque.

show abstract

Time-Variant Digital Twin Modeling through the Kalman-Generalized Sparse Identification of Nonlinear Dynamics

Cited by 12 publications

References 19 publications

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

Digital Twin Model of Electric Drives Empowered by EKF

Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation

Contact Info

Product

Resources

About