A Rolling Bearing Fault Diagnosis Method Based on Multimodal Knowledge Graph

In contemporary industries, diagnosing bearing faults is crucial, yet the complexity and diversity of these faults pose challenges to traditional methods. Existing algorithms typically treat compound faults as independent events, overlooking the interrelations among different faults, which constrains the performance in diagnosing the faults with diverse semantic complexities. Also, the research on leveraging multimodal data to enhance fault diagnosis accuracy is limited. To overcome the weakness mentioned above, a multimodal knowledge graph (MKG) construction method based on multimodel data, including time series vibration signals, spectrum, and description text of datasets, is proposed. Subsequently, a fault diagnosis method utilizing a MKG completion model based on a relation cascade graph attention network is designed to capture the relationship between various faults. Experimental results on an MKG constructed from seven bearing datasets demonstrate the robustness of the proposed method.