The effects of temperature fluctuations in the impedance measurements’ spectral estimates confuse the procedures to distinguish actual states’ classification, demanding compensation. The present paper demonstrates a new method to achieve temperature compensation based on a Transfer Component Analysis (TCA), a subtype of transfer learning, of the features from a source domain (in a well-known labeled condition) to another target domain (in an unknown condition). This procedure assumes only the labeled features data in the healthy condition (baseline) and damaged state in a specific known temperature as source data. The features computed are the Root Mean Square Deviation (RMSD) indices of the real and imaginary impedance signals. A machine-learning algorithm based on Mahalanobis squared distance (\(\mathcal D^{2}\)) is trained using the features computed from the baseline condition in the reference temperature. Also, the other temperature and structural conditions data are assumed as testing data of the target condition. TCA’s main idea is mapping the features from the original features space to a new subspace where the detection becomes possible using the same training data in the source domain. The results performed in a testbench with a piezoelectric element (PZT) bonded under a set of temperatures monitored, and simulated damage confirmed that the proposed method could recognize the real states correctly by transferring the knowledge from the features of the source domain into the target domain, assuming different temperatures.

阻抗测量的频谱估计中温度波动的影响混淆了区分实际状态分类的程序，需要补偿。本文展示了一种实现温度补偿的新方法，该方法基于从源域（在众所周知的标记条件下）到另一个目标域（在一个情况不明）。此过程仅将特定已知温度下健康状况（基线）和损坏状态的标记特征数据假定为源数据。计算的特征是实部和虚部阻抗信号的均方根偏差 (RMSD) 指数。基于马氏距离平方的机器学习算法 ( \(\mathcal D^{2}\)) 使用根据参考温度中的基线条件计算的特征进行训练。此外，其他温度和结构条件数据被假定为目标条件的测试数据。TCA 的主要思想是将特征从原始特征空间映射到一个新的子空间，在该子空间中使用源域中的相同训练数据可以进行检测。在一组温度监测下结合压电元件 (PZT) 和模拟损伤的测试台上执行的结果证实，所提出的方法可以通过将知识从源域的特征转移到目标域来正确识别真实状态，假设不同的温度。