A novel residual graph representation learning method towards multi-source data fusion and fault diagnosis of machinery

作者：Zhuojun Dai, Weidong Xu, Zhuyun Chen, Kairu Wen, Bin Zhang, Yi He, Weihua Li · 发表于：Measurement Science and Technology · 年份：2025 · DOI：10.1088/1361-6501/adc4fb · 被引用次数：7 · 研究领域：Advanced Decision-Making Techniques、Fault Detection and Control Systems、Evaluation and Optimization Models

Abstract The harmonic reducer, a critical component in industrial mechanical systems, is responsible for high-precision motion transmission. Timely fault detection and diagnosis are essential to prevent catastrophic safety incidents and ensure system reliability. However, existing fault diagnosis methods often fail to fully utilize the rich information embedded in multi-source data, leading to suboptimal performance. Additionally, the interactions and dependencies between different data channels remain inadequately captured, hindering accurate fault identification. To address these limitations, this paper introduces a novel fault diagnosis framework based on residual graph representation learning and multi-source data fusion. The proposed model monitors the harmonic reducer’s operational state using a multi-sensor network. A signal preprocessing module, leveraging fast fourier transform and RadiusGraph, transforms raw multi-sensor data into a graph structure, where nodes represent sensor channels and weighted edges capture interdependencies. This graph structure effectively reflects the interaction and dependency among multi-channel data. For feature extraction, we propose a bi-layer ChebyNet with residual connections to mine and update the relationships between sensor data while addressing challenges such as gradient vanishing or explosion in deep graph neural networks. Finally, the learned graph representations are used to classify fault types, enabling precise identificati...