Abstract The prognostic study of the ball screw is critical to increase the reliability of manufacturing system, which has drawn great attention in the field of Prognostics and Health Management (PHM). The particle filters (PF) method is a powerful tool for prognostic study because of its capability of robustly predicting the future behavior. However, lack of analytical ball screw measurement model limits the application of PF. In this paper, an ensemble GRU network is designed to extend PF to the case where the analytical measurement equation is not available. The proposed hybrid GRU-PF method integrates the data-driven model and the physical model into the particle filters network to realize the prognostic and remaining useful life (RUL) prediction of the ball screw. The effectiveness of the proposed method is validated by designing a ball screw accelerated degradation test (ADT), and the results of this experimental study demonstrate the satisfactory performances in terms of prognostic sensibility.

中文翻译：

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集成数据驱动粒子滤波器对滚珠丝杠的预测研究

摘要 滚珠丝杠的预测研究对于提高制造系统的可靠性至关重要，在预测与健康管理（PHM）领域引起了极大的关注。粒子过滤器 (PF) 方法是一种强大的预后研究工具，因为它能够稳健地预测未来的行为。然而，分析滚珠丝杠测量模型的缺乏限制了PF的应用。在本文中，设计了一个集成 GRU 网络以将 PF 扩展到解析测量方程不可用的情况。所提出的混合 GRU-PF 方法将数据驱动模型和物理模型集成到粒子过滤器网络中，以实现滚珠丝杠的预测和剩余使用寿命 (RUL) 预测。