Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on over-parameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.

翻译：构建能够快速适应实时工况监测（CM）信号的预测模型对于工程系统/单元至关重要。然而，当前许多方法在在线场景中面临表示能力与敏捷性之间的权衡。例如，假设CM信号服从特定函数形式的参数化方法虽便于高效在线预测更新，但这种简化会导致模型对假设的脆弱性，且难以捕捉复杂信号。另一方面，基于过参数化或非参数模型的方法虽擅长解释复杂非线性信号，但其实时更新却极具挑战性。本文提出一种基于神经过程的方法来解决这一权衡问题。该方法将CM信号中的可用观测值编码至表示空间，进而重构信号的历史演化规律以实现预测。训练完成后，模型可编码任意数量的观测值而无需重新训练，从而支持带不确定性量化的即时实时预测，并能随在线数据积累便捷更新。此外，模型创新性地整合了来自个体单元的定性信息（即标签）。这种融合不仅增强了每个单元的个性化预测，还实现了信号与其关联标签的联合推断。基于可靠性工程中合成数据与真实数据的数值研究表明，本模型在实时自适应、带不确定性量化的增强信号预测以及标签-信号联合预测方面展现出显著优势。