# Super-Frequency Sampling for Thermal Transient Analysis
## 5 Conclusions  
The enhancement of temporal resolution in thermographic transient measurements by super-frequency sampling represents a significant improvement for thermographic analysis. This technique enables a more detailed analysis of thermal transients due to an increased sampling density. An analysis of the thermal transients of a periodically  
Figure 8: (**left**) Cumulative distribution of the true sampling periods, \(\tau_{\text{true}}\), obtained for all 506 possible test areas on top of the diode (Figure 4 (left)). (**right**) Cumulative distribution of the medians of the reconstructed sampling periods, \(\Delta\bar{\tau}_{\text{r}}\).  
Figure 9: Average signal-to-noise ratio, \(\overline{\text{SNR}}\), of the different \(N\times N\) test areas. The error bar indicates the range between the 5 th and the 95 th percentiles, \(Q_{0.05}\) and \(Q_{0.95}\), of the signal-to-noise ratios of the input signals. The average signal-to-noise ratio, for which all sub-areas converge to the same sampling period, \(\tau_{\text{true}}\), is indicated by the dashed line.  
switched SMD test diode shows an increase in sampling frequency by a factor of 10. While the sampling frequency of the infrared camera system in use was limited to 1 kHz, the sampling frequency could be increased to 10 kHz, i. e., a temporal resolution of 100 ms.  
The limitation of this algorithm is its long measurement time, which makes it important to consider a possible slow thermal drift of the measurement setup. To minimize the influence of thermal drift, the measurement environment should be well insulated from external thermal influences. Furthermore, long measurement times can lead to a large amount of infrared data, which can be computationally demanding to handle. These effects as well as the temporal accuracy of the measurement device limit the practically achievable speed-up factors.  
The benefit of super-frequency sampling is of particular importance in the field of power-electronics and other small components. (i) High-frequency sampling in the infrared spectral range enables the monitoring of fast thermal transitions with increased clarity. An enhanced temporal resolution at full spatial resolution is of particular importance for the identification of hot spots and thermal gradients, yielding a better approach for thermal management strategies. (ii) Super-frequency sampling is also useful for the validation of computational fluid dynamics (CFD) simulations. By providing empirical data that closely match the high-resolution temporal dynamics of thermal processes, super-frequency sampling enables the fine-tuning of CFD models, ensuring that simulations more accurately reflect real-world conditions. This synergy enhances predictive capabilities, allowing for more precise design optimizations and validations before physical prototypes are tested. (iii) The early detection of potential thermal issues, made possible through a detailed analysis, allows engineers to implement design and testing phase adjustments. Such proactive measures are critical in applications where high-power densities are prevalent, such as vehicle electrification and aerospace, ensuring that the reliability and performance of power electronics are optimized.  
Conceptualization, S.H.A., N.J.Z., P.W.N. and S.S.; Formal analysis, S.H.A.; Investigation, S.H.A.; Writing--original draft, S.H.A.; Writing--review & editing, N.J.Z., P.W.N. and S.S.; Supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Hella GmbH & Co. KGaA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.