Mechanical machines face structural damage problems during their service life. Structural damage can severely affect safety and functionality of the structure and lead to economic loss. In this work, a damage identification scheme is developed by combining waveform chain code (WCC) analysis and hierarchical cluster analysis based on complex network theory. Waveform chain code analysis was carried out using the principal component analysis reduced frequency response function (PCA-reduced FRF), and the areas under the slope differential value curves were calculated as damage-sensitive WCC features. Unsupervised machine learning using hierarchical cluster analysis was then conducted on these damage-sensitive features. A rectangular Perspex plate was studied using the newly developed damage identification scheme as an example. Experimental results showed that the proposed scheme can successfully separate all the damage conditions from the undamaged state with 100% accuracy. In terms of damage severity and location identification, the proposed scheme is sensitive to detect damage severity with damage index as low as 0.17. In addition, combination of PCA-reduced FRF and mode shapes showed positive correlation between the magnitude of the resonant peak and the displacement of the impact point in identifying different damage locations of the plate.

中文翻译：

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使用 PCA 降频响应函数和波形链码分析的无监督损伤识别方案

机械机器在其使用寿命期间面临结构损坏问题。结构损坏会严重影响结构的安全性和功能性，并导致经济损失。在这项工作中，结合波形链码（WCC）分析和基于复杂网络理论的层次聚类分析，开发了一种损伤识别方案。采用主成分分析降频响应函数（PCA-reduced FRF）进行波形链码分析，计算斜率微分曲线下面积作为损伤敏感的WCC特征。然后对这些损伤敏感特征进行使用层次聚类分析的无监督机器学习。以新开发的损伤识别方案为例，研究了矩形有机玻璃板。实验结果表明，所提出的方案能够以 100% 的准确率成功地将所有损坏状态与未损坏状态分离。在损伤严重程度和位置识别方面，该方案对损伤严重程度检测敏感，损伤指数低至 0.17。此外，PCA 降低的 FRF 和振型的组合表明，在识别板的不同损坏位置时，共振峰的大小与冲击点的位移之间存在正相关关系。