Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.

环境影响是导波结构健康监测系统的重大挑战。这些影响会扭曲信号并增加误报的可能性。许多研究论文研究了在实验室中可重现的导波数据集中常见变化的缓解策略，例如温度和压力。在更不可预测的户外条件下检测损坏的研究和策略较少。本文提出了一种长短期主成分分析重建方法，用于检测降水、冰冻等高度变化环境下的合成损伤。该方法不需要任何温度或其他补偿方法，并通过 2 年收集的大约 700 万导波测量值进行测试。