Virtual Sensing System of Structural Vibration using Digital Twin

Oh, Seungin; Park, Daeun; Hyunwoo, Baek; kimseunghun,; Lee, Jai-Kyung; Kim, Jin-Gyun

doi:10.5050/ksnve.2020.30.2.149

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…where δT s,m and δ∆P m are the temperature and the heat source change vectors with zero mean, respectively, and are calculated by δY m , which is the measurement noise data with zero mean. This equation can be calculated more efficiently through the Krylov model reduction method introduced in (7). Using (34), we can redefine (29) as follows:…”

Section: A Regularization Parameter For Stabilitymentioning

confidence: 99%

Virtual Thermal Sensor for Real-Time Monitoring of Electronic Packages in a Totally Enclosed System

Ahn

Kim

et al. 2022

IEEE Access

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A monitoring system is essential for controlling temperatures under safe levels of operation. It is often challenging to attach temperature sensors directly to drive chips owing to the operating environment or geometric challenges. Based on this motivation, we present a model-based virtual thermal sensing technique for the real-time temperature monitoring of the electronics package. A few real sensors located far from the target position are utilized in this virtual sensing system. These are then connected to a well-tuned finite element model for data augmentation utilizing an inverse heat conduction framework. Therefore, the virtual sensor allows us to estimate the temperature without the aid of a sensor installed inside. However, this technique has a stability issue because it is classified into an inverse problem (i.e., an ill-posed problem). We propose a Tikhonov regularization method to address this challenge, including an efficient ridge estimator. The ridge estimator is used to select an optimal regularization parameter so that we can obtain the stable and reliable inverse solution. Since conventional ridge estimators rely on total transient errors, they require a significant computation. The proposed estimator is based on the bias and variance errors, not the total errors, which allow us to efficiently find the optimal parameter. In this paper, the thermal model is modeled using the finite element method, and the Krylov subspace-based model order reduction is employed to reduce the computational burden. Finally, the proposed virtual thermal sensor was experimentally validated utilizing a sealed cylindrical structure in which the commercial servo drive operated.INDEX TERMS Electronic packages, finite element method, inverse heat conduction problem, ridge estimator, Tikhonov regularization, virtual thermal sensor.

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Section: A Regularization Parameter For Stabilitymentioning

confidence: 99%

Virtual Thermal Sensor for Real-Time Monitoring of Electronic Packages in a Totally Enclosed System

Ahn

Kim

et al. 2022

IEEE Access

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“…For structural dynamics and vibration problems, modelbased virtual sensing techniques [17][18][19][20] have been studied using various state identifcation algorithms such as Markov parameter-based and Kalman flter family methods. Law et al [21] used an explicit time integration scheme to identify the moving forces in the time domain, and Zhu and Law implemented a method to identify the moving forces on multispan bridges [22,23].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Response Estimation of Structural Vibration with Inverse Force Identification

Lee

et al. 2023

Structural Control and Health Monitoring

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Virtual sensing is an effective method to identify the inaccessible state of the structural systems by compensating the limitations of the conventional physical sensing techniques. Recently, it becomes popular in structural vibration field and their relevant physical domains such as civil, mechanical, aerospace engineering, thermal dynamics, and acoustics. This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling are used to avoid two times larger degrees of freedom within the state-space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the numerical test, the system could identify the status of the motor housing structure in the speed of 16,181 S a m p l e s / s . The FDE and RMSE values are bounded under 0.1816 and 1503.2. In the case of the experimental test, the algorithm is implemented to the beam structure using a single-board computer, including inverse force identification and unmeasured response prediction. Even in the limited computational environment, the system could identify the applied forces in real time in the speed of 2,173 S a m p l e s / s . Through all experimental cases, FDE and RMSE values are bounded under 0.2925 and 0.1660. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.

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“…For structural dynamics and vibration problems, model-based virtual sensing techniques 17,18,19 have been studied using various state identification algorithms such as Markov parameter-based and Kalman filter family methods. Law et al 20 used an explicit time integration scheme to identify the moving forces in the time domain, and Zhu and Law implemented a method to identify the moving forces on multi-span bridges 21,22 .…”

Section: Introductionmentioning

confidence: 99%

Real-Time Estimation of Unmeasured Vibro-acoustic Responses Using Inverse Force Identification Technique

Cho

Lee

et al. 2022

Topics in Modal Analysis &Amp; Parameter Identification, Volume 8

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This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration.The second-order ordinary differential formulation and its projection-based reducedorder modeling is used to avoid two times larger degrees of freedom within the state space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the experimental test, the algorithm is implemented on a single-board computer, including inverse force identification and unmeasured response prediction. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.

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Virtual Sensing System of Structural Vibration using Digital Twin

Cited by 8 publications

References 14 publications

Virtual Thermal Sensor for Real-Time Monitoring of Electronic Packages in a Totally Enclosed System

Virtual Thermal Sensor for Real-Time Monitoring of Electronic Packages in a Totally Enclosed System

Real-Time Response Estimation of Structural Vibration with Inverse Force Identification

Real-Time Estimation of Unmeasured Vibro-acoustic Responses Using Inverse Force Identification Technique

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