A novel graph structure data-driven crack damage identification for compressor blade based on vibro-acoustic signal

The evolution of advanced sensing techniques and intelligent algorithms has significantly underpinned the growth of structural health monitoring and damage identification. Modern industry equipment like compressors, which are indispensable to the petrochemical and other process industries, usually operate under complex conditions including variable speed. The more vulnerable compressor components, such as the blades, are prone to diverse levels of damage over time. Existing research usually discusses the damage identification problem of blades under the Euclidean space, facing challenges in linking multi-source heterogeneous signals. This study introduces a novel approach, employing a graph-structured data-based method for identifying compressor blade cracks. It specifically focuses on variable rotating speed conditions, subsequently proposing an intelligent identification framework based on vibro-acoustic graph-structured data. Firstly, the affinity graphs made of vibro-acoustic damage signal are constructed to express the latent damage information beyond Euclidean space after Fourier transform and residual learning-based feature extraction for one-dimensional data. Then the developed multi-order graph convolutional network and domain discriminator layers are used to extract the domain-invariant damage features, which will be fed into the linear layer for class prediction. Finally, the method's efficacy is cross-verified through experiments with actual measurements on a compressor platform, specifically focusing on variable rotating speed cases.