There is a growing interest in using permanently installed sensors to monitor for defects in engineering components; the ability to collect real-time measurements is valuable when evaluating the structural integrity of the monitored component. However, a challenge in evaluating the detection capabilities of a permanently installed sensor arises from its fixed location and finite field-of-view, combined with the uncertainty in damage location. A probabilistic framework for evaluating the detection capabilities of a permanently installed sensor is thus proposed. By combining the spatial maps of sensor sensitivity obtained from model-assisted methods and probability of defect location obtained from structural mechanics, the expectation and confidence in the probability of detection (POD) can be estimated. The framework is demonstrated with four sensor-component combinations, and the results show the ability of the framework to characterise the detection capability of permanently installed sensors and quantify its performance with metrics such as the \({\mathrm{a}}_{90|95}\) value (the defect size where there is 95% confidence of obtaining at least 90% POD), which is valuable for structural integrity assessments as a metric for the largest defect that may be present and undetected. The framework is thus valuable for optimising and qualifying monitoring system designs in real-life engineering applications.

使用永久安装的传感器来监测工程部件中的缺陷越来越受到关注；在评估受监控组件的结构完整性时，收集实时测量值的能力很有价值。然而，评估永久安装传感器检测能力的挑战来自其固定位置和有限视场，以及损坏位置的不确定性。因此提出了一种用于评估永久安装传感器检测能力的概率框架。通过结合从模型辅助方法获得的传感器灵敏度空间图和从结构力学获得的缺陷位置概率，可以估计检测概率 (POD) 的期望和置信度。\({\mathrm{a}}_{90|95}\)值（有 95% 置信度获得至少 90% POD 的缺陷大小），这对于结构完整性评估是有价值的，作为最大的度量可能存在但未被发现的缺陷。因此，该框架对于优化和验证现实工程应用中的监控系统设计很有价值。