发表机构
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对柔性压阻式应变传感器的误差问题，本文提出结合物理信息概率逆模型与三状态监测器的可靠性框架，经测试提升拟合精度与覆盖率，可有效捕捉超范围及异常状况，无需标记故障样本。
AI 中文摘要
柔性压阻式应变传感器因具备柔韧性与顺应性，成为可穿戴设备与软体机器人应用中最常用的传感方案之一。然而，其电阻响应具有非线性与迟滞特性，传感器可能超出校准工作范围，或在范围内出现异常，将误差带入决策或控制回路。概率回归模型虽能追踪置信度，却忽略了上述极限；即便读数来自传感器超出容许范围或内部已故障的情况，预测均值仍可能表现正常，因此看似置信的估计值不等同于可靠值。本文提出一种可靠性框架，将基于物理引导输入特征构建的物理信息概率逆模型，与融合不确定性、应变及应变速率极限的风险因子相结合，形成三状态监测器。对镍钛诺丝与银涂覆聚酰胺线的测试显示，采用高斯过程的拟合得分提升至0.90-0.95（均方根误差0.26%-0.15%），经验覆盖率达96%，符合95%的目标；该监测器捕捉了95%的超范围情况与100%的异常状况，且在正常操作下保持可靠。传感器在输出估计值的同时报告置信度，可让系统采取弃权（不执行）操作，且无需收集软体材料难以获取的标记故障样本。
英文摘要
Soft piezoresistive strain sensors are one of the most common sensing solutions for wearable and soft robotic applications due to their flexibility and compliance. However, their resistance response is nonlinear and hysteretic, and a sensor can be pushed past its calibrated workspace or misbehave inside it, carrying that error into a decision or control loop. Probabilistic regressors track confidence but ignore those limits. A predictive mean can look unremarkable even when the reading comes from a sensor outside its admissible range or already failing internally, so a confident-looking estimate is not the same as a trustworthy one. This paper proposes a reliability framework pairing a physics-informed probabilistic inverse model, built on physics-guided input features, with a risk factor fusing uncertainty with strain and strain-rate limits into a three-state monitor. Tests on a Nitinol wire and a silver-coated polyamide thread with a Gaussian Process raised fit scores to 0.90-0.95 (RMSE 0.26%-0.15%) and a 96% empirical coverage against the 95% target. The monitor caught 95% of out-of-range and 100% of abnormal conditions while staying reliable under nominal operation. A sensor that reports confidence alongside its estimate lets a system withhold action instead, since it needs no labeled failure examples, which are hard to collect for soft materials.
