Neural Factorization-based Bearing Fault Diagnosis
This work addresses a critical safety issue in high-speed train operation by improving fault diagnosis accuracy, though it appears incremental as it builds on existing neural factorization principles.
The paper tackles the problem of insufficient diagnostic accuracy for bearing fault diagnosis in high-speed trains under complex conditions by proposing a Neural Factorization-based Classification (NFC) framework, which achieves superior diagnostic performance compared to traditional machine learning methods.
This paper studies the key problems of bearing fault diagnosis of high-speed train. As the core component of the train operation system, the health of bearings is directly related to the safety of train operation. The traditional diagnostic methods are facing the challenge of insufficient diagnostic accuracy under complex conditions. To solve these problems, we propose a novel Neural Factorization-based Classification (NFC) framework for bearing fault diagnosis. It is built on two core idea: 1) Embedding vibration time series into multiple mode-wise latent feature vectors to capture diverse fault-related patterns; 2) Leveraging neural factorization principles to fuse these vectors into a unified vibration representation. This design enables effective mining of complex latent fault characteristics from raw time-series data. We further instantiate the framework with two models CP-NFC and Tucker-NFC based on CP and Tucker fusion schemes, respectively. Experimental results show that both models achieve superior diagnostic performance compared with traditional machine learning methods. The comparative analysis provides valuable empirical evidence and practical guidance for selecting effective diagnostic strategies in high-speed train bearing monitoring.