Detecting damage at an early stage can avoid a serious catastrophic failure of structures due to inevitable cause, such as fatigue, environmental corrosion, and natural disasters. Various damage detection algorithms have been proposed based on autoregressive model using time series data, which are computationally expensive, and the selection of an optimal order of the model requires extra expertise. In this paper, computationally efficient algorithm is proposed to process the time series data using principal component analysis (PCA) in an effective way. PCA is utilized to model a feature space to compute damage sensitive features insensitive to environmental variations and measurement noise. The modeled feature space preserves the damage information along with eliminates the consequences of environmental variations and measurement noise. Furthermore, Mahalanobis squared distance is adopted to compute damage index as the severity of the damage. The proposed method is validated on analytical models of IASC–ASCE benchmark structure. The test results show that the proposed damage diagnosis method can be useful for wireless sensor network-based structural health monitoring with less computation and low data transmission rate.

尽早发现损坏可以避免由于不可避免的原因（例如疲劳，环境腐蚀和自然灾害）而导致的结构严重灾难性故障。基于使用时间序列数据的自回归模型，已经提出了各种损伤检测算法，这在计算上是昂贵的，并且模型的最佳顺序的选择需要额外的专业知识。本文提出了一种计算有效的算法，可以有效地利用主成分分析（PCA）处理时间序列数据。PCA用于对特征空间建模，以计算对环境变化和测量噪声不敏感的对损伤敏感的特征。建模的特征空间可保留损坏信息，并消除环境变化和测量噪声的后果。此外，采用马氏距离平方距离来计算损伤指数作为损伤的严重程度。IASC–ASCE基准结构的分析模型对所提出的方法进行了验证。测试结果表明，所提出的损伤诊断方法可用于基于无线传感器网络的结构健康监测，计算量少，数据传输速率低。