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.

翻译：实时状态监测对于复杂系统的可靠高效运行至关重要。然而，完全依赖物理传感器可能因其成本、安装限制或无法直接测量某些关键参数而受到制约。虚拟传感通过利用易于获取的传感器数据和系统知识来估计不可访问的参数或推断系统状态，从而应对这些限制。工业系统日益复杂，需要部署多种模态的传感器以全面理解系统状态。这些传感器以不同频率捕获数据，以监测快速变化和缓慢变化的系统动态，以及系统的局部和全局状态演变。这导致了异构的时序动态，尤其是在变化的运行和环境条件下，这对实现精确的虚拟传感构成了重大挑战。为此，我们提出了一种异构时序图神经网络（HTGNN）框架。HTGNN显式地建模来自不同传感器的信号，并将运行条件集成到模型架构中。我们使用两个新发布的数据集评估HTGNN：一个包含多种负载条件的轴承数据集用于轴承负载预测，以及一个为期一年的模拟数据集用于预测桥梁活荷载。我们的结果表明，HTGNN在这两项任务中均显著优于已有的基线方法，尤其是在高度变化的运行条件下。这些结果突显了HTGNN作为一种稳健且精确的复杂系统虚拟传感方法的潜力，为改进监测、预测性维护和提升系统性能铺平了道路。我们的代码和数据可在 https://github.com/EPFL-IMOS/htgnn 获取。