Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics

2024-07-26

arXiv.org

AI Summary

Real-time state monitoring in complex systems is hindered by high sensor costs, deployment constraints, and unmeasurable critical parameters; existing virtual sensing approaches fail to model temporal couplings among heterogeneous sensors with mismatched sampling rates, disparate dynamic scales, and varying operating conditions. Method: We propose the Heterogeneous Temporal Graph Neural Network (HTGNN), the first framework to explicitly model the joint dynamic relationship between multimodal signals and operational conditions, overcoming limitations of conventional time-alignment paradigms. HTGNN integrates four core techniques: graph-structured modeling, temporal-adaptive encoding, multi-scale dynamic aggregation, and operating-condition-aware embedding. Contribution/Results: Evaluated on two newly constructed benchmark datasets—bearing load monitoring and bridge live-load estimation—HTGNN significantly outperforms state-of-the-art methods, achieving over 32% reduction in prediction error under highly variable operating conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain critical parameters. Virtual sensing addresses these limitations by leveraging readily available sensor data and system knowledge to estimate inaccessible parameters or infer system states. The increasing complexity of industrial systems necessitates deployments of sensors with diverse modalities to provide a comprehensive understanding of system states. These sensors capture data at varying frequencies to monitor both rapid and slowly varying system dynamics, as well as local and global state evolutions of the systems. This leads to heterogeneous temporal dynamics, which, particularly under varying operational end environmental conditions, pose a significant challenge for accurate virtual sensing. To address this, we propose a Heterogeneous Temporal Graph Neural Network (HTGNN) framework. HTGNN explicitly models signals from diverse sensors and integrates operating conditions into the model architecture. We evaluate HTGNN using two newly released datasets: a bearing dataset with diverse load conditions for bearing load prediction and a year-long simulated dataset for predicting bridge live loads. Our results demonstrate that HTGNN significantly outperforms established baseline methods in both tasks, particularly under highly varying operating conditions. These results highlight HTGNN's potential as a robust and accurate virtual sensing approach for complex systems, paving the way for improved monitoring, predictive maintenance, and enhanced system performance. Our code and data are available under https://github.com/EPFL-IMOS/htgnn.

Problem

• Addressing heterogeneous temporal dynamics in complex systems.
• Estimating inaccessible parameters using virtual sensing techniques.
• Improving accuracy in virtual sensing under varying operational conditions.

Innovation

• HTGNN models diverse sensor signals effectively
• Integrates operating conditions into model architecture
• Outperforms baselines in varying operational conditions