Zhao Jiaxin, Wu Xing, Liu Chang, He Feifei
Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.
Faculty of Mechanical & Electrical Engineering, Kunming University of Science &Technology, Kunming 650500, China.
Sensors (Basel). 2025 Jul 28;25(15):4664. doi: 10.3390/s25154664.
Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis.
不同运行条件导致的数据分布差异显著，损害了泛化能力，而多源数据中未充分利用的时空信息则阻碍了特征提取。为解决这一问题，我们提出了一种用于变工况诊断的时空协同感知驱动的特征图构建与拓扑挖掘方法。首先，利用故障特征的运行条件不变性和跨条件一致性，我们使用单源数据和相似性聚类构建故障特征图，验证不同条件下的拓扑相似性和表示一致性。其次，我们揭示多源特征拓扑中的时空相关性。通过时空协同感知将多源时空信息嵌入故障特征图中，我们基于谱相似性建立高维时空特征拓扑图，将广义特征表示扩展到时空域。最后，我们开发了一种图残差卷积网络，以挖掘复杂运行条件下多源时空特征的拓扑信息。在变工况/多工况数据集上的实验表明：特征图将多源信息与运行变化无缝集成；该方法精确捕捉了由振动方向/路径差异引起的时空延迟；所提出的模型在复杂运行条件下保持了高诊断准确率和强泛化能力，为旋转机械故障诊断提供了一个高度可靠的框架。
