Accurate prediction of remaining useful life (RUL) is essential to enhance system reliability and reduce maintenance risk. Yet many strong contemporary models are fragile around fault onset and opaque to engineers: short, high-energy spikes are smoothed away or misread, fixed thresholds blunt sensitivity, and physics-based explanations are scarce. To remedy this, we introduce SARNet (Spike-Aware Consecutive Validation Framework), which builds on a Modern Temporal Convolutional Network (ModernTCN) and adds spike-aware detection to provide physics-informed interpretability. ModernTCN forecasts degradation-sensitive indicators; an adaptive consecutive threshold validates true spikes while suppressing noise. Failure-prone segments then receive targeted feature engineering (spectral slopes, statistical derivatives, energy ratios), and the final RUL is produced by a stacked RF--LGBM regressor. Across benchmark-ported datasets under an event-triggered protocol, SARNet consistently lowers error compared to recent baselines (RMSE 0.0365, MAE 0.0204) while remaining lightweight, robust, and easy to deploy.

翻译：精确预测剩余使用寿命（RUL）对于提升系统可靠性和降低维护风险至关重要。然而，许多当前先进的模型在故障起始阶段表现脆弱且对工程师而言缺乏透明度：短暂的高能量尖峰被平滑处理或误读，固定阈值降低了灵敏度，且基于物理解释的说明稀缺。为解决这些问题，本文提出了SARNet（尖峰感知连续验证框架）。该框架基于现代时间卷积网络（ModernTCN），并增加了尖峰感知检测功能，以提供基于物理信息的可解释性。ModernTCN预测对退化敏感的指标；自适应连续阈值用于验证真实尖峰，同时抑制噪声。随后，针对易失效片段进行定向特征工程（如谱斜率、统计导数、能量比），最终的RUL由堆叠的RF--LGBM回归器生成。在事件触发协议下的基准移植数据集上，与近期基线相比，SARNet在保持轻量级、鲁棒性强且易于部署的同时，持续降低了预测误差（RMSE 0.0365，MAE 0.0204）。