A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines

Intelligent fault diagnosis technology, as a promising approach, is gradually playing an irreplaceable role in ensuring the safety, reliability, and efficiency of mechanical equipment. However, in real-world industrial scenarios, obtaining adequate high-quality label information is typically challenging and unrealistic, resulting in the performance degradation of most existing supervised learning-based diagnosis models, and necessitating the development of unsupervised intelligent diagnostic models. In addition, the sample independence hypothesis is widely used in existing studies, which significantly ignores the further mining of relevant auxiliary information between samples and its positive effect on performance improvement. To overcome these challenges, a novel intelligent fault diagnosis framework, called the convolutional capsule auto-encoder-based unsupervised directed hierarchical graph network with clustering representation (CCAE-UDHGN-CR), is established and employed in unlabeled scenarios. First, a novel convolutional capsule auto-encoder (CCAE), which combines reconstruction loss and semantic clustering loss, is constructed and used to extract deep coding features that contain attribute information of the sample itself. Then, with the assistance of cosine similarity measurement strategy, the internal correlation between samples is fully mined, and on this basis, the conversion of deep coding features to the graph sample set is realized, which serves as the input of the subsequent unsupervised directed hierarchical graph network (UDHGN). Finally, the deep representation features extracted by the UDHGN are further fed into the density-based spatial clustering of applications with noise (DBSCAN) model to complete the determination of category information. A total of three cases based on key functional components and manipulator are employed for performance verification. The comprehensive diagnosis results all show that the proposed CCAE-UDHGN-CR model can effectively alleviate the dependence on label information while maintaining excellent diagnosis performance.