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The state-of-the-art determines the remaining useful lifetime (RUL) through a steady-state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resulted in considerable RUL prediction errors in the real world. However, the dynamic changing conditions (e.g., large temperature swings) may affect the degradation evolution of SiC MOSFET, which could eventually result in RUL changes. Thus, it must be integrated to make accurate predictions. To precisely understand the RUL variation complexity, the junction temperature (Tj) has been measured with a Negative Thermal Coefficient (NTC) thermistor, Temperature Sensitive Electrical Parameter (TSEP), and these profiles have been modeled through the thermal model RC foster network using Extended Kalman Filter (EKF). Then, the on-state resistance (Rds,on) variations and Degradation Acceleration Factor (DAF) under the dynamic environment conditions are integrated into a lifetime prediction model to accurately predict the RUL through the Long Short-Term Memory (LSTM) machine learning algorithm. 

Abstract Assouad–Nagata dimension addresses both large‐ and small‐scale behaviors of metric spaces and is a refinement of Gromov's asymptotic dimension. A metric space is a minor‐closed metric if there exists an (edge‐)weighted graph satisfying a fixed minor‐closed property such that the underlying space of is the vertex‐set of , and the metric of is the distance function in . Minor‐closed metrics naturally arise when removing redundant edges of the underlying graphs by using edge‐deletion and edge‐contraction. In this paper, we determine the Assouad–Nagata dimension of every minor‐closed metric. Our main theorem simultaneously generalizes known results about the asymptotic dimension of ‐minor free unweighted graphs and about the Assouad–Nagata dimension of complete Riemannian surfaces with finite Euler genus (Bonamy et al., Asymptotic dimension of minor‐closed families and Assouad–Nagata dimension of surfaces,JEMS(2024)). 

Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (Tj), on-state voltage (Vds,on), on-state resistance (Rds,on), and case temperature (TC) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically.