无需标签即可自动发现未知故障模式，实时更新预测模型。
Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
- 用贝叶斯非参数混合模型无监督发现故障模式
- 迭代更新机制使模型可动态合并或扩展故障类型
- 适合高复杂度制造场景的在线健康监测
现代制造系统常面临多种不可预测的故障行为，但现有预测模型多假设故障模式固定且标签已知，限制了数字孪生在高混线或自适应生产环境中的应用。针对此问题，本文提出一种新型贝叶斯非参数框架，结合狄利克雷过程混合模块实现无监督故障模式发现，与基于神经网络的预测模块协同工作。核心创新在于双向迭代反馈机制，两模块相互更新，随新数据动态推断、合并或扩展故障模式，同时保持高预测精度。在仿真和航空发动机数据集上的实验表明，该方法性能优于或媲美现有方法，具备强在线适应能力，适用于复杂制造环境下基于数字孪生的系统健康管理。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。