通过贝叶斯非参数过程混合进行多传感器系统的故障预测：未知和未标记的故障模式
Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
AI总结：
本文提出了一种基于贝叶斯非参数过程混合的预测框架，用于处理多传感器系统中未知和未标记故障模式的预测问题，通过迭代反馈机制实现故障模式的动态推断和预测准确性提升。
摘要：
现代制造系统常常经历多种不可预测的故障行为，但大多数现有预测模型假设故障模式是固定且已知的，并且有标记的历史数据。这种假设限制了数字孪生在预测维护中的应用，特别是在高多样性或适应性生产环境中，新的故障模式可能产生，且故障模式标签可能不可用。为了解决这些挑战，我们提出了一种新的贝叶斯非参数框架，该框架结合了狄利克雷过程混合模块用于无监督的故障模式发现和基于神经网络的预测模块。关键创新在于一个迭代反馈机制，用于联合学习两个模块。这些模块通过迭代更新彼此，动态推断、扩展或合并故障模式，随着新数据的到来，同时提供高预测准确性。在模拟和飞机发动机数据集上的实验表明，所提出的方法在与现有方法竞争或显著优于现有方法。它还表现出强大的在线适应能力，使其在复杂制造环境中非常适合数字孪生系统健康管理系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
