Hierarchical graph neural network with adaptive cross-graph fusion for remaining useful life prediction. (22nd February 2023)

• Record Type: Journal Article
• Authors: Wang, Gang; Zhang, Yanan; Lu, Mingfeng; Wu, Zhangjun
• Is Part Of: Measurement science & technology. Volume 34:Number 5(2023)
• Publication Date: 2023-02-22
• Subjects: hierarchical graph -- graph neural network -- deep learning -- remaining useful life prediction; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/acb83e
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Multi-sensor monitoring data provide abundant information resources for complex machine systems, which facilitates monitoring the degradation process of machinery and ensuring the reliability of the industrial process. However, previous prognostic methods focus more on the sequential data obtained from multi-sensors, while ignoring the underlying prior structural information of the equipment. To fully leverage the structural information into the modeling process, and thus improve the remaining useful life (RUL) prediction performance, a hierarchical graph neural network with adaptive cross-graph fusion (HGNN-ACGF) method for RUL prediction is proposed in this study. In the HGNN-ACGF method, a hierarchical graph consisting of a sensor graph and a module graph is constructed by introducing the structural information to fully model the degradation trend information of the complex machine system. Besides, the graph neural network (GNN) is adopted to learn the representation at both the module graph and sensor graph, and an adaptive cross-graph fusion (ACGF) block is proposed. Owing to the cross-graph fusion block, the representation from different graphs can be fused adaptively by considering the relative importance between different modules and sensors. To verify the proposed method, the experiments were conducted on a set of degradation data sets of aircraft engines provided by the Commercial Modular Aero-Propulsion System Simulation. The experimental results show that the proposed method has superior performance in RUL prediction over the state-of-the-art methods.