Modern manufacturing systems often experience multiple and unpredictable failure behaviors, yet most existing prognostic models assume a fixed, known set of failure modes with labeled historical data. This assumption limits the use of digital twins for predictive maintenance, especially in high-mix or adaptive production environments, where new failure modes may emerge, and the failure mode labels may be unavailable. To address these challenges, we propose a novel Bayesian nonparametric framework that unifies a Dirichlet process mixture module for unsupervised failure mode discovery with a neural network-based prognostic module. The key innovation lies in an iterative feedback mechanism to jointly learn two modules. These modules iteratively update one another to dynamically infer, expand, or merge failure modes as new data arrive while providing high prognostic accuracy. Experiments on both simulation and aircraft engine datasets show that the proposed approach performs competitively with or significantly better than existing approaches. It also exhibits robust online adaptation capabilities, making it well-suited for digital-twin-based system health management in complex manufacturing environments.

翻译：现代制造系统常出现多种不可预测的失效行为，然而现有的大多数剩余寿命预测模型均假设存在固定且已知的失效模式集合，并依赖已标记的历史数据。这一假设限制了数字孪生在预测性维护中的应用，特别是在高混合或自适应生产环境中——此类环境中可能出现新的失效模式，且失效模式标签往往难以获取。为应对这些挑战，我们提出了一种新颖的贝叶斯非参数框架，该框架将用于无监督失效模式发现的狄利克雷过程混合模块与基于神经网络的剩余寿命预测模块相统一。其核心创新在于通过迭代反馈机制实现两个模块的联合学习：随着新数据的不断输入，这两个模块通过相互迭代更新，动态推断、扩展或合并失效模式，同时保持较高的预测精度。在仿真数据集与航空发动机数据集上的实验表明，所提方法的性能与现有方法相当或显著优于现有方法。该方法还展现出稳健的在线适应能力，使其非常适用于复杂制造环境中基于数字孪生的系统健康管理。