SHAP-based Reliable Threshold Decision-driven Remaining Useful Life Prediction for MOSFETs

LIU Jinfeng; WU Qiuxue; HERBERT Ho-Ching Iu

doi:10.11999/JEIT251379

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LIU Jinfeng, WU Qiuxue, HERBERT Ho-Ching Iu. SHAP-based Reliable Threshold Decision-driven Remaining Useful Life Prediction for MOSFETs[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251379

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LIU Jinfeng, WU Qiuxue, HERBERT Ho-Ching Iu. SHAP-based Reliable Threshold Decision-driven Remaining Useful Life Prediction for MOSFETs[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251379

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SHAP-based Reliable Threshold Decision-driven Remaining Useful Life Prediction for MOSFETs

doi: 10.11999/JEIT251379 cstr: 32379.14.JEIT251379

1.
Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080, China
2.
School of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley , WA 6009, Australia

Funds: The Natural Science Foundation of Heilongjiang Province (LH2023E086)

Received Date: 2025-12-30
Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-20

Available Online: 2026-05-13

Abstract

Abstract

To address the disconnect between conventional fixed-threshold early warning methods for power MOSFETs and their physical failure mechanisms, this paper proposes a lifetime prediction framework that integrates Explainable Artificial Intelligence (XAI). First, an adaptive dual-threshold partitioning strategy is designed by combining K-means clustering with the Proximal Policy Optimization (PPO) algorithm. The initial solution obtained by K-means is used as the search starting point. A multi-objective reward function is then constructed to balance interval proportion, state-transition sensitivity, and threshold-spacing penalties. This function guides the agent in threshold optimization and enables accurate partitioning of degradation stages. Second, SHAPley additive explanations (SHAP) analysis is introduced to improve the interpretability of the black-box decision-making process. It verifies the rationality of threshold decisions from the perspective of feature-mechanism correlations. The results show that the low threshold is mainly governed by steady-state features in the healthy stage and meets the safety baseline requirement. The high threshold is dominated by dynamic features of late-stage accelerated degradation and accurately identifies the critical point. These findings confirm the reliability and transparency of the threshold decisions. Based on this framework, an early warning mechanism is triggered when degradation data exceed the reliable low threshold. A Residual-connected Stacked Gated Recurrent Unit (R-SGRU) is then used for Remaining Useful Life (RUL) prediction. Experiments on the NASA dataset show that the proposed model outperforms several baseline models, including Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN). The test-set Mean Squared Error (MSE) is below 0.001 5, and R² is above 0.98. This study provides accurate and reliable decision support for early warning in MOSFETs. It also links data features with physical mechanisms through explainable techniques, supporting the development of trustworthy artificial intelligence for device prognostics. Objective This study addresses two key issues in power MOSFET lifetime prognostics: the disconnect between conventional fixed-threshold early warning methods and physical mechanisms, and the limited interpretability of existing approaches. A framework integrating adaptive dual-threshold partitioning with XAI is proposed to support predictive maintenance with both physical credibility and high prediction accuracy. Methods An adaptive dual-threshold partitioning strategy is proposed by integrating K-means clustering with PPO reinforcement learning. Threshold positions are optimized using a multi-objective reward function to accurately identify degradation stages. SHAP analysis is used to quantify the contributions of 13-dimensional morphological features based on Shapley values. This validates the physical rationality of threshold decisions from a mechanistic perspective. When degradation data exceed the low threshold, an early warning is triggered. The R-SGRU network is then used for RUL prediction by capturing long-term dependencies through its gating mechanism. The proposed method is validated using the NASA dataset, forming a complete technical route from intelligent early warning to accurate prediction. Results and Discussions The thresholds optimized by PPO achieve the best performance across all metrics (Table 1). SHAP analysis reveals the physical rationale for the threshold decisions. In the healthy stage, the low threshold is mainly governed by steady-state features. By contrast, the high threshold is determined by accelerated degradation dynamics. This result establishes a quantitative correlation between data-driven results and physical failure mechanisms. SHAP interaction heatmaps (Figs. 6 and 7) further show the synergistic effects among features. Device failure is a complex process driven by the coordinated evolution of multiple features. The R-SGRU prediction model based on the optimized thresholds shows excellent performance on the NASA dataset (Table 5). Across the four device groups, the model achieves an MSE below 0.001 5 and an R2 above 0.98, outperforming the baseline models. Conclusions This study proposes an XAI-based framework for predicting the RUL of power MOSFETs. For threshold partitioning, an adaptive dual-threshold strategy combining K-means clustering and PPO reinforcement learning is adopted. A multi-objective reward function enables accurate identification of nonlinear degradation stages, and its performance is validated across four test devices. For interpretability, SHAP analysis provides mechanistic support for threshold decisions. The results show that low thresholds depend on steady-state features in the healthy period, whereas high thresholds are dominated by late-stage accelerated degradation features. This pattern is consistent with actual failure mechanisms. Feature interaction heatmaps reveal complex cooperative effects among multiple features and improve the understanding of the decision-making process. The R-SGRU prediction model shows strong time-series modeling capability and ensures high stability and accuracy. This work establishes a complete technical route from intelligent early warning to accurate prediction. It achieves adaptive threshold optimization and links data-driven results with physical mechanisms through interpretability analysis. The findings provide reliable support for the intelligent operation and maintenance of power MOSFETs.

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References(16)

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