Solving inverse problems with autoencoders on learnt graphs

Majumdar, Angshul

doi:10.1016/j.sigpro.2021.108300

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…The proposed VTS is based on the inverse heat conduction problem (IHCP) [9], [10]. The inverse problem is the process of calculating from a set of measurements and is widely used in image reconstruction [11]- [14] and virtual sensing [5]- [8]. Developing the VTS starts from modeling the heat transfer model.…”

Section: Introductionmentioning

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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