To perform long-term structural health monitoring, a method based on a nonlinear autoregressive exogenous network is used to learn the features present in signals acquired from a pristine structure. When a subsequent measured signal is input to the trained nonlinear autoregressive exogenous network, the output is a prediction of the equivalent signal from a pristine structure. The residual when the pristine predicted signal is subtracted from the measured signal is used for defect detection and localization. A methodology of how to train, test and assess a nonlinear autoregressive exogenous network for guided wave signals is introduced and applied to experimental data obtained over a period of 8 years from a sparse array of guided wave sensors deployed on a steel storage tank. A separate nonlinear autoregressive exogenous model is trained for each sensor pair in the array using data captured in 2012. The method is first tested using data from a single pair of sensors. Defect signals are synthesized by superposing simulated responses from defects onto later experimental signals obtained from the real structure. The test results for the nonlinear autoregressive exogenous method show better detection performance than those from the optimal baseline selection method, in terms of receiver operating characteristic curves. The detection performance of the nonlinear autoregressive exogenous method is further assessed on signals from the whole sensor array, again with simulated defect responses superposed. It is shown that good detection and localization performance can be achieved by combining the nonlinear autoregressive exogenous residual signals from different sensor pairs. The nonlinear autoregressive exogenous method is tested on experimental data acquired at intervals over the following 7 years as the condition of the tank naturally degrades. Indications from localized corrosion are observed. Finally, an artificial localized anomaly is added to the tank and is visible at the correct location in the image formed using the nonlinear autoregressive exogenous method.

为了执行长期结构健康监测，使用基于非线性自回归外源网络的方法来学习从原始结构获取的信号中存在的特征。当随后的测量信号输入到经过训练的非线性自回归外生网络时，输出是对原始结构等效信号的预测。从测量信号中减去原始预测信号时的残差用于缺陷检测和定位。介绍了如何训练、测试和评估用于导波信号的非线性自回归外生网络的方法，并将其应用于从部署在钢储罐上的稀疏导波传感器阵列中获得的 8 年时间的实验数据。使用 2012 年捕获的数据为阵列中的每个传感器对训练单独的非线性自回归外生模型。该方法首先使用来自单对传感器的数据进行测试。通过将来自缺陷的模拟响应叠加到从真实结构获得的后续实验信号上来合成缺陷信号。就接收者操作特性曲线而言，非线性自回归外源方法的测试结果显示出比最佳基线选择方法更好的检测性能。非线性自回归外生方法的检测性能进一步评估来自整个传感器阵列的信号，再次叠加模拟缺陷响应。结果表明，通过组合来自不同传感器对的非线性自回归外生残差信号可以实现良好的检测和定位性能。非线性自回归外生方法在接下来 7 年随着水箱状况自然退化而每隔一段时间获得的实验数据进行测试。观察到局部腐蚀的迹象。最后，将人工局部异常添加到储罐中，并在使用非线性自回归外生方法形成的图像中的正确位置可见。