A damage identification scheme combining impact-synchronous modal analysis (ISMA) and artificial neural network is developed in this study. The ISMA de-noising method makes it feasible to detect and classify the damage states with high accuracy when the machine is under operation. The feed-forward backprop network was utilized in this study. The input feature vector of the network consisted of the FRF changes in a selected vibrational mode frequency interval at several measurement points. The scheme was tested on a rectangular Perspex plate. It is proved that the trained network can successfully identify damage locations with the testing data collected by ISMA, which allows the damage detection to be carried out without shutting down the tested machine. For the plate structure in this study, an overall accuracy reached 100% when all five measurement points were used. With the input features optimized by mode shape assessment, 100% accuracy was also achieved with only two measurement points.

中文翻译：

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利用去噪频率响应函数和人工神经网络的操作损伤识别方案

本研究开发了一种结合冲击同步模态分析 (ISMA) 和人工神经网络的损伤识别方案。ISMA去噪方法使得在机器运行时对损坏状态进行高精度检测和分类成为可能。本研究使用了前馈反向传播网络。网络的输入特征向量由几个测量点处选定的振动模式频率间隔内的 FRF 变化组成。该方案在矩形有机玻璃板上进行了测试。事实证明，经过训练的网络可以通过 ISMA 收集的测试数据成功识别损坏位置，从而可以在不关闭被测机器的情况下进行损坏检测。对于本研究中的板结构，使用所有五个测量点时，总体准确度达到 100%。通过振型评估优化输入特征，仅用两个测量点也实现了 100% 的准确度。