用神经分解法从振动信号中挖掘轴承故障特征，提升高铁诊断精度。
Neural Factorization-based Bearing Fault Diagnosis
- 将振动时序嵌入多模式潜在特征向量，捕捉多样故障模式。
- 通过神经分解融合特征，实现原始数据中复杂故障特征的有效挖掘。
- 在高铁轴承诊断中表现优于传统方法，适合工业故障监测场景。
本文研究高速列车轴承故障诊断的关键问题。作为列车运行系统的核心部件，轴承健康状况直接关系到行车安全。传统诊断方法在复杂工况下诊断精度不足。为此，提出一种基于神经分解的分类框架（NFC），核心思想包括：1）将振动时序嵌入多个模式的潜在特征向量，以捕捉多样化的故障相关模式；2）利用神经分解原理，将这些向量融合为统一的振动表示。该设计能有效从原始时序数据中挖掘复杂潜在故障特征。进一步基于CP和Tucker分解方案，构建了两种模型CP-NFC与Tucker-NFC。实验表明，两者均显著优于传统机器学习方法，为高铁轴承监测中的有效诊断策略选择提供了有价值的实证依据与实践指导。
原文摘要 · Abstract (English)
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.
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